Data augmentation for detour path configuring

ABSTRACT

This application is directed to augmenting training images used for generating vehicle driving models. A computer system obtains a first image of a road, identifies within the first image a drivable area of the road, obtains an image of a traffic safety object, and determines a detour path on the drivable area. The computer system determines positions of a plurality of traffic safety objects to be placed adjacent to the detour path, and generates a second image from the first image by adaptively overlaying a respective copy of the image of the traffic safety object at each of the positions of the plurality of traffic safety objects on the drivable area within the first image. The second image is added to a corpus of training images to be used by a machine learning system to generate a model for facilitating driving a vehicle (e.g., at least partial autonomously).

RELATED APPLICATIONS

This application is related to U.S. patent application Ser. No.17/855,623, titled “Data Augmentation for Detour Vehicle Control”, filedJun. 30, 2022, and U.S. patent application Ser. No. 17/855,670, titled“Data Augmentation for Driver Monitoring”, filed Jun. 30, 2022, each ofwhich is incorporated by reference in its entirety.

TECHNICAL FIELD

The present application generally relates to vehicle technology, andmore particularly to, computer-aided methods and systems for augmentingexisting training data applied to train a deep learning model for atleast partial autonomous vehicle control.

BACKGROUND

Vehicles are now capable of self-driving with different levels ofautonomy. Each of these levels is characterized by the amount of humanand autonomous control. For example, The Society of Automotive Engineers(SAE) defines 6 levels of driving automation ranging from 0 (fullymanual) to 5 (fully autonomous). These levels have been adopted by theU.S. Department of Transportation.

There are numerous advantages of autonomous vehicles, including: (1)lowering the number of vehicles on the roads (most privately ownedvehicles are driven a small fraction of the time); (2) more predictableand safer driving behavior than human driven vehicles; (3) lessemissions if more vehicles are electrically powered; (4) improved fuelefficiency; (5) increased lane capacity; (6) shorter travel times; and(7) mobility for users who are incapable of diving. One of the keyobstacles facing the autonomous vehicle industry, however, is thecomplexity and unpredictability of road and traffic conditions. Thismakes it difficult to train autonomous vehicles for every possible rarecondition or event that the vehicle may encounter while driving(so-called “edge” cases). For example, occasionally, human drivers mayneed to react to extraordinary or rare events, like a package fallingoff of a truck, a lane closure, or something even more rare, such as anaircraft making an emergency landing on the freeway. In these raresituations, human drivers are usually able to react instinctively toavoid harm to themselves and their vehicles. However, unless anautonomous driving model has been trained for each such rare event, thevehicle may not know how to react.

To capture and learn from existing road and traffic conditions, fleetoperators often collect large amounts of data from individual vehicles.This data is regularly sent from the vehicles to a remote server andlater analyzed. Transmitting such large amounts of data (e.g., HD videoor LIDAR data) from many vehicles (e.g., over a cellular data network)consumes valuable communication bandwidth and is prohibitivelyexpensive. Therefore, it is desirable to provide a more efficientmechanism for collecting, monitoring, and learning from road conditiondata captured by a fleet of vehicles.

Additionally, the large amounts of data collected by individual vehiclesare often processed in real time using deep learning techniques. Thesedeep learning techniques are trained using training data that waspreviously collected under different traffic conditions. Collection ofthorough and high-quality training data is costly in time and money, andthe training data collected in real life is often insufficient or has alow quality. Data inferred by the deep learning techniques oftentimeshas a limited accuracy level because of the insufficient or low-qualitytraining data used in training. It would be beneficial to have a moreefficient mechanism to train and apply deep learning techniques tofacilitate vehicle driving.

SUMMARY

This application is directed to methods, systems, and non-transitorycomputer readable storage media for augmenting training data used totrain models that facilitate driving of a vehicle (e.g., models forobject perception and analysis, vehicle localization and environmentmapping, vehicle drive control, vehicle drive planning, and localoperation monitoring). Training data augmentation can be implemented indifferent levels. For example, in simple augmentation, at least onetransformation of geometry, color, or kernel is applied to images usedin vehicle model training. In complex augmentation, part of an image isoptionally replaced with a portion from another image, and new featuresor information may be added to an image without changing remainingfeatures in the image. In some situations, an entirely new scenario iscreated in an image by data augmentation, and the augmented trainingdata is applied in vehicle model training. Such data augmentation ishighly scalable and can be implemented at a low cost and with a quickturnaround time. This improves diversity and quantity of the trainingdata, providing high fidelity coverage of more driving scenarios.

In one aspect, a method is implemented at a computer system includingone or more processors and memory to augment training data used forvehicle driving modelling. The method includes obtaining a first imageof a road, identifying within the first image a drivable area of theroad, obtaining an image of an object, generating a second image fromthe first image by overlaying the image of the object over the drivablearea, and adding the second image to a corpus of training images to beused by a machine learning system to generate a model for facilitatingdriving of a vehicle. In some embodiments, the method further includestraining, using machine learning, a model using the corpus of trainingimages, including the second image, and distributing the model to one ormore vehicles. In use, the model is configured to process road imagescaptured by a first vehicle to facilitate driving the first vehicle(e.g., at least partially autonomously).

In one aspect, a method is implemented at a computer system includingone or more processors and memory to augment training images used formonitoring vehicle drivers. The method includes obtaining a first imageof a first driver in an interior of a first vehicle and separating, fromthe first image, a first driver image from a first background image ofthe interior of the first vehicle. The method further includes obtaininga second background image and generating a second image by overlayingthe first driver image on the second background image. The methodfurther includes adding the second image to a corpus of training imagesto be used by a machine learning system to generate a model formonitoring vehicle drivers. In some embodiments, the model formonitoring vehicle drivers is configured to determine whether a vehicledriver is looking forward at the road ahead of the vehicle. In someembodiments, the model for monitoring vehicle drivers is configured todetermine whether a vehicle driver is looking forward at the road,looking to the left, looking to the right, looking down, closing his/hereyes, or talking.

In some embodiments, the method further includes collecting a pluralityof background images and clustering the plurality of background imagesto generate a plurality of image clusters. The method further includesidentifying a set of one or more remote images that are most distant inthe plurality of image clusters and selecting the first background imagefrom the set of remote images.

In one aspect, a method is implemented at a computer system includingone or more processors and memory to augment training images used forgenerating vehicle driving models. The method includes obtaining a firstimage of a road, identifying within the first image a drivable area ofthe road, obtaining an image of a traffic safety object (e.g., a cone, adelineator, or a barrel), determining a detour path on the drivablearea, and determining positions for a plurality of traffic safetyobjects to be placed adjacent to the detour path. The method furtherincludes generating a second image from the first image by adaptivelyoverlaying a respective copy of the image of the traffic safety objectat each of the determined positions. The method further includes addingthe second image to a corpus of training images to be used by a machinelearning system to generate a model for facilitating driving of avehicle. In some embodiments, the method further includes training themodel by machine learning using the corpus of training images. Thetraining images include the second image. The model is distributed toone or more vehicles, including a first vehicle. In use, the model isconfigured to process road images captured by the first vehicle tofacilitate driving the vehicle (e.g., at least partially autonomously).

According to another aspect of the present application, a computersystem includes one or more processing units and memory having aplurality of programs stored in the memory. The programs, when executedby the one or more processing units, cause the vehicle to perform any ofthe methods for augmenting training data and facilitating vehicledriving as described above.

According to another aspect of the present application, a non-transitorycomputer readable storage medium stores a plurality of programsconfigured for execution by a computer system having one or moreprocessing units. The programs, when executed by the one or moreprocessing units, cause the computer system to perform any of themethods for augmenting training data and facilitating vehicle driving asdescribed above.

BRIEF DESCRIPTION OF DRAWINGS

The accompanying drawings, which are included to provide a furtherunderstanding of the embodiments, are incorporated herein, constitute apart of the specification, illustrate the described embodiments, and,together with the description, serve to explain the underlyingprinciples.

FIG. 1 is an example vehicle driving environment for a plurality ofvehicles, in accordance with some embodiments.

FIG. 2 is a block diagram of an example vehicle configured to be drivenwith a certain level of autonomy, in accordance with some embodiments.

FIG. 3 is a block diagram of an example server for monitoring andmanaging vehicles in a vehicle driving environment, in accordance withsome embodiments.

FIG. 4 is a block diagram of a machine learning system for training andapplying vehicle data processing models for facilitating at leastpartial autonomous driving of a vehicle, in accordance with someembodiments.

FIG. 5A is a structural diagram of an example neural network applied toprocess vehicle data in a vehicle data processing model, in accordancewith some embodiments, and FIG. 5B is an example node in the neuralnetwork, in accordance with some embodiments.

FIG. 6 is a flow diagram of an example process for augmenting trainingimages by overlaying an image of an object on a drivable area of a roadin an image, in accordance with some embodiments.

FIGS. 7A-7C are three images in which each drivable area is marked andimages of objects are extracted, in accordance with some embodiments.

FIG. 8A is a first image having a drivable area of a road marked with aplurality of road markings, in accordance with some embodiments, andFIG. 8B is a second image that is generated from the first image,including an image of an obstacle vehicle, in accordance with someembodiments.

FIG. 9A is an image having a plurality of vehicles on a drivable area ofa road, in accordance with some embodiments. FIG. 9B is a diagram ofresult lines recognized from the image using a drivable area detectionmodel that is trained without synthetic patching, and FIG. 9C is adiagram of result lines recognized from the image using a drivable areadetection model that is trained with synthetic patching, in accordancewith some embodiments. FIG. 9D is an image having a plurality ofvehicles on a drivable area, in accordance with some embodiments.

FIG. 9E is a diagram of result lines recognized from the image using adrivable area detection model that is trained without syntheticpatching, and FIG. 9F is a diagram of result lines recognized from theimage using a drivable area detection model that is trained withsynthetic patching, in accordance with some embodiments.

FIGS. 10A-10C are three images showing a process for adding one or moreimages of vehicles to a shoulder area of a road in a first image, inaccordance with some embodiments.

FIG. 11A is an example image showing that a vehicle image located on aroad area is copied and added to a shoulder area, in accordance withsome embodiments, and FIG. 11B is another example image 604 showing thatone or more images of vehicles located on a road area are copied andadded to a shoulder area, in accordance with some embodiments.

FIG. 12 is a flow diagram of a process for adding an uncommonly seenobject onto an image, in accordance with some embodiments.

FIGS. 13A-13E are five images including distinct example images ofuncommonly seen objects added onto a drivable area of a road, inaccordance with some embodiments.

FIG. 14 is a flow diagram of a process for augmenting training data usedfor vehicle driving modelling, in accordance with some embodiments.

FIG. 15 is a flow diagram of an example process for augmenting atraining image by replacing a background image of the training image, inaccordance with some embodiments.

FIG. 16 is a flow diagram of an example process for separating aforeground driver image from a first image using a segmentation model,in accordance with some embodiments.

FIG. 17 is a flow diagram of an example process for augmenting driverimages, in accordance with some embodiments.

FIG. 18 is a flow diagram of another example process for augmentingdriver images, in accordance with some embodiments.

FIG. 19 is a two-dimensional (2D) clustering plot showing an exampledistribution of representations of a plurality of background images, inaccordance with some embodiments.

FIG. 20 is a flow diagram of an example method for augmenting trainingimages used for generating a model for monitoring vehicle drivers, inaccordance with some embodiments.

FIG. 21 is an example training image showing a drivable area of a roadonto which copies of an image of a traffic safety object are placed, inaccordance with some embodiments.

FIG. 22 is a flow diagram of an example process for augmenting trainingimages with traffic safety objects and training a model using thetraining images, in accordance with some embodiments.

FIG. 23 is a flow diagram of an example process for augmenting trainingimages by overlaying images of traffic safety objects on a drivable areaof a road in an image, in accordance with some embodiments.

FIGS. 24A-24C are three example training images including a plurality oftraffic safety objects, in accordance with some embodiments, and FIGS.24D-24F are top views of a drivable area of a road in the trainingimages in FIGS. 24A-24C, in accordance with some embodiments.

FIGS. 25A-25C are another three example training images including aplurality of traffic safety objects, in accordance with someembodiments, and FIGS. 25D-25F are top views of a drivable area of aroad in the training images in FIGS. 25A-25C, in accordance with someembodiments.

FIGS. 26A-26F are six training images showing a drivable area of a roadwhere copies of an image of a traffic safety object are placed to definedistinct detour paths, in accordance with some embodiments.

FIG. 27 is a flow diagram of another example method for augmentingtraining data used for generating autonomous vehicle driving modelling,in accordance with some embodiments.

Like reference numerals refer to corresponding parts throughout theseveral views of the drawings.

DETAILED DESCRIPTION

Reference will now be made in detail to specific embodiments, examplesof which are illustrated in the accompanying drawings. In the followingdetailed description, numerous non-limiting specific details are setforth in order to assist in understanding the subject matter presentedherein. But it will be apparent to one of ordinary skill in the art thatvarious alternatives may be used without departing from the scope of theclaims and the subject matter may be practiced without these specificdetails. For example, it will be apparent to one of ordinary skill inthe art that the subject matter presented herein can be implemented onmany types of electronic devices with digital video capabilities.

Various embodiments of this application are directed to augmentingtraining data (particularly road images) used to train models thatfacilitate driving of a vehicle. Existing training data is expanded by(1) adding one or more road features (e.g., a vehicle or a pedestrian)on a drivable area of a road, (2) adding traffic safety objects (e.g.,cones, delineators, barrels, flashers, or reflectors) on a drivable areaof a road, and/or (3) changing a foreground image or a background imageof a driver image. Such augmented training data can be used locally (orat a remote server) by a machine learning system of the vehicle to traina model for facilitating driving of a vehicle (e.g., for occluded lanedetection, generic-obstacle detection, shoulder vehicle detection,and/or cone-based detour path detection). Such data augmentation ishighly scalable and can be implemented at a low cost with a quickturnaround time, thereby allowing entirely new scenarios to be createdin training data and applied in vehicle model training. By these means,the diversity and quantity of the training data can be convenientlyimproved to provide a high fidelity coverage of more driving scenariosand make sure that the models are trained with high quality trainingdata and can output accurate deep learning results to facilitate vehicledriving.

FIG. 1 is an example vehicle driving environment 100 having a pluralityof vehicles 102 (e.g., vehicles 102P, 102T, and 102V), in accordancewith some embodiments. Each vehicle 102 has one or more processors,memory, a plurality of sensors, and a vehicle control system. Thevehicle control system is configured to sense the vehicle drivingenvironment 100 and drive on roads having different road conditions. Theplurality of vehicles 102 may include passenger cars 102P (e.g.,sport-utility vehicles and sedans), vans 102V, trucks 102T, anddriver-less cars. Each vehicle 102 can collect sensor data and/or userinputs, execute user applications, present outputs on its userinterface, and/or operate the vehicle control system to drive thevehicle 102. The collected data or user inputs can be processed locally(e.g., for training and/or for prediction) at the vehicle 102 and/orremotely by one or more servers 104. The one or more servers 104 providesystem data (e.g., boot files, operating system images, and userapplications) to the vehicle 102, and in some embodiments, process thedata and user inputs received from the vehicle 102 when the userapplications are executed on the vehicle 102. In some embodiments, thevehicle driving environment 100 further includes storage 106 for storingdata related to the vehicles 102, servers 104, and applications executedon the vehicles 102.

For each vehicle 102, the plurality of sensors includes one or more of:(1) a global positioning system (GPS) sensors; (2) a light detection andranging (LiDAR) scanner; (3) one or more cameras; (4) a radio detectionand ranging (RADAR) sensor; (5) an infrared sensor; (6) one or moreultrasonic sensors; (7) a dedicated short-range communication (DSRC)module; (8) an inertial navigation system (INS) including accelerometersand gyroscopes; and/or (9) an odometry sensor. The cameras areconfigured to capture a plurality of images in the vehicle drivingenvironment 100, and the plurality of images are applied to map thevehicle driving environment 100 to a 3D vehicle space and identify alocation of the vehicle 102 within the environment 100. The cameras alsooperate with one or more other sensors (e.g., GPS, LiDAR, RADAR, and/orINS) to localize the vehicle 102 in the 3D vehicle space. For example,the GPS identifies a geographical position (geolocation) of the vehicle102 on the Earth, and the INS measures relative vehicle speeds andaccelerations between the vehicle 102 and adjacent vehicles 102. TheLiDAR scanner measures the distance between the vehicle 102 and adjacentvehicles 102 and other objects. Data collected by these sensors is usedto determine vehicle locations determined from the plurality of imagesor to facilitate determining vehicle locations between two images.

The vehicle control system includes a plurality of actuators for atleast steering, braking, controlling the throttle (e.g., accelerating,maintaining a constant velocity, or decelerating), and transmissioncontrol. Depending on the level of automation, each of the plurality ofactuators (or manually controlling the vehicle, such as by turning thesteering wheel) can be controlled manually by a driver of the vehicle,automatically by the one or more processors of the vehicle, or jointlyby the driver and the processors. When the vehicle 102 controls theplurality of actuators independently or jointly with the driver, thevehicle 102 obtains the sensor data collected by the plurality ofsensors, identifies adjacent road features in the vehicle drivingenvironment 100, tracks the motion of the vehicle, tracks the relativedistance between the vehicle and any surrounding vehicles or otherobjects, and generates vehicle control instructions to at leastpartially autonomously control driving of the vehicle 102. Conversely,in some embodiments, when the driver takes control of the vehicle, thedriver manually provides vehicle control instructions via a steeringwheel, a braking pedal, a throttle pedal, and/or a gear lever directly.In some embodiments, a vehicle user application is executed on thevehicle and configured to provide a user interface. The driver providesvehicle control instructions to control the plurality of actuators ofthe vehicle control system via the user interface of the vehicle userapplication. By these means, the vehicle 102 is configured to drive withits own vehicle control system and/or the driver of the vehicle 102according to the level of autonomy.

In some embodiments, autonomous vehicles include, for example, a fullyautonomous vehicle, a partially autonomous vehicle, a vehicle withdriver assistance, or an autonomous capable vehicle. Capabilities ofautonomous vehicles can be associated with a classification system, ortaxonomy, having tiered levels of autonomy. A classification system canbe specified, for example, by industry standards or governmentalguidelines. For example, the levels of autonomy can be considered usinga taxonomy such as level 0 (momentary driver assistance), level 1(driver assistance), level 2 (additional assistance), level 3(conditional assistance), level 4 (high automation), and level 5 (fullautomation without any driver intervention) as classified by theInternational Society of Automotive Engineers (SAE International).Following this example, an autonomous vehicle can be capable ofoperating, in some instances, in at least one of levels 0 through 5.According to various embodiments, an autonomous capable vehicle mayrefer to a vehicle that can be operated by a driver manually (that is,without the autonomous capability activated) while being capable ofoperating in at least one of levels 0 through 5 upon activation of anautonomous mode. As used herein, the term “driver” may refer to a localoperator or a remote operator. The autonomous vehicle may operate solelyat a given level (e.g. level 2 additional assistance or level 5 fullautomation) for at least a period of time or during the entire operatingtime of the autonomous vehicle. Other classification systems can provideother levels of autonomy characterized by different vehiclecapabilities.

In some embodiments, the vehicle 120 drives in the vehicle drivingenvironment 100 at level 5. The vehicle 120 collects sensor data fromthe plurality of sensors, processes the sensor data to generate vehiclecontrol instructions, and controls the vehicle control system to drivethe vehicle autonomously in response to the vehicle controlinstructions. Alternatively, in some situations, the vehicle 120 drivesin the vehicle driving environment 100 at level 0. The vehicle 120collects the sensor data and processes the sensor data to providefeedback (e.g., a warning or an alert) to a driver of the vehicle 120 toallow the driver to drive the vehicle 120 manually and based on thedriver's own judgement. Alternatively, in some situations, the vehicle120 drives in the vehicle driving environment 100 partially autonomouslyat one of levels 1-4. The vehicle 120 collects the sensor data andprocesses the sensor data to generate a vehicle control instruction fora portion of the vehicle control system and/or provide feedback to adriver of the vehicle 120. The vehicle 102 is driven jointly by thevehicle control system of the vehicle 102 and the driver of the vehicle102. In some embodiments, the vehicle control system and driver of thevehicle 102 control different portions of the vehicle 102. In someembodiments, the vehicle 102 determines the vehicle status. Based on thevehicle status, a vehicle control instruction of one of the vehiclecontrol system or driver of the vehicle 102 preempts or overridesanother vehicle control instruction provided by the other one of thevehicle control system or driver of the vehicle 102.

For the vehicle 102, the sensor data collected by the plurality ofsensors, the vehicle control instructions applied to the vehicle controlsystem, and the user inputs received via the vehicle user applicationform a collection of vehicle data 112. In some embodiments, at least asubset of the vehicle data 112 from each vehicle 102 is provided to oneor more servers 104. A server 104 provides a central vehicle platformfor collecting and analyzing the vehicle data 112, monitoring vehicleoperation, detecting faults, providing driving solutions, and updatingadditional vehicle information 114 to individual vehicles 102 or clientdevices 108. In some embodiments, the server 104 manages vehicle data112 of each individual vehicle 102 separately. In some embodiments, theserver 104 consolidates vehicle data 112 from multiple vehicles 102 andmanages the consolidated vehicle data jointly (e.g., the server 104statistically aggregates the data).

Additionally, in some embodiments, the vehicle driving environment 100further includes one or more client devices 108, such as desktopcomputers, laptop computers, tablet computers, and mobile phones. Eachclient device 108 is configured to execute a client user applicationassociated with the central vehicle platform provided by the server 104.The client device 108 is logged into a user account on the client userapplication, and the user account is associated with one or morevehicles 102. The server 104 provides the collected vehicle data 112 andadditional vehicle information 114 (e.g., vehicle operation information,fault information, or driving solution information) for the one or moreassociated vehicles 102 to the client device 108 using the user accountof the client user application. In some embodiments, the client device108 is located in the one or more vehicles 102, while in otherembodiments, the client device is at a location distinct from the one ormore associated vehicles 102. As such, the server 104 can apply itscomputational capability to manage the vehicle data and facilitatevehicle monitoring and control on different levels (e.g., for eachindividual vehicle, for a collection of vehicles, and/or for relatedclient devices 108).

The plurality of vehicles 102, the one or more servers 104, and the oneor more client devices 108 are communicatively coupled to each other viaone or more communication networks 110, which is used to providecommunications links between these vehicles and computers connectedtogether within the vehicle driving environment 100. The one or morecommunication networks 110 may include connections, such as a wirednetwork, wireless communication links, or fiber optic cables. Examplesof the one or more communication networks 110 include local areanetworks (LAN), wide area networks (WAN) such as the Internet, or acombination thereof. The one or more communication networks 110 are, insome embodiments, implemented using any known network protocol,including various wired or wireless protocols, such as Ethernet,Universal Serial Bus (USB), FIREWIRE, Long Term Evolution (LTE), GlobalSystem for Mobile Communications (GSM), Enhanced Data GSM Environment(EDGE), code division multiple access (CDMA), time division multipleaccess (TDMA), Bluetooth, Wi-Fi, voice over Internet Protocol (VoIP),Wi-MAX, or any other suitable communication protocol. A connection tothe one or more communication networks 110 may be established eitherdirectly (e.g., using 3G/4G connectivity to a wireless carrier), orthrough a network interface (e.g., a router, a switch, a gateway, a hub,or an intelligent, dedicated whole-home control node), or through anycombination thereof. In some embodiments, the one or more communicationnetworks 110 allow for communication using any suitable protocols, likeTransmission Control Protocol/Internet Protocol (TCP/IP). In someembodiments, each vehicle 102 is communicatively coupled to the servers104 via a cellular communication network.

In some embodiments, deep learning techniques are applied by thevehicles 102, the servers 104, or both, to process the vehicle data 112.For example, in some embodiments, after image data is collected by thecameras of one of the vehicles 102, the image data is processed using anobject detection model to identify objects (e.g., road featuresincluding, but not limited to, vehicles, lane lines, shoulder lines,road dividers, traffic lights, traffic signs, road signs, cones,pedestrians, bicycles, and drivers of the vehicles) in the vehicledriving environment 100. In some embodiments, additional sensor data iscollected and processed by a vehicle control model to generate a vehiclecontrol instruction for controlling the vehicle control system. In someembodiments, a vehicle planning model is applied to plan a drivingcontrol process based on the collected sensor data and the vehicledriving environment 100. The object detection model, vehicle controlmodel, and vehicle planning model are collectively referred to herein asvehicle data processing models, each of which includes one or moreneural networks. In some embodiments, such a vehicle data processingmodel is applied by the vehicles 102, the servers 104, or both, toprocess the vehicle data 112 to infer associated vehicle status and/orprovide control signals. In some embodiments, a vehicle data processingmodel is trained by a server 104, and applied locally or provided to oneor more vehicles 102 for inference of the associated vehicle statusand/or to provide control signals. Alternatively, a vehicle dataprocessing model is trained locally by a vehicle 102, and appliedlocally or shared with one or more other vehicles 102 (e.g., by way ofthe server 104). In some embodiments, a vehicle data processing model istrained in a supervised, semi-supervised, or unsupervised manner.

FIG. 2 is a block diagram of an example vehicle 102 configured to bedriven with a certain level of autonomy, in accordance with someembodiments. The vehicle 102 typically includes one or more processingunits (CPUs) 202, one or more network interfaces 204, memory 206, andone or more communication buses 208 for interconnecting these components(sometimes called a chipset). The vehicle 102 includes one or more userinterface devices. The user interface devices include one or more inputdevices 210, which facilitate user input, such as a keyboard, a mouse, avoice-command input unit or microphone, a touch screen display, atouch-sensitive input pad, a gesture capturing camera, or other inputbuttons or controls. Furthermore, in some embodiments, the vehicle 102uses a microphone and voice recognition or a camera and gesturerecognition to supplement or replace the keyboard. In some embodiments,the one or more input devices 210 include one or more cameras, scanners,or photo sensor units for capturing images, for example, of a driver anda passenger in the vehicle 102. The vehicle 102 also includes one ormore output devices 212, which enable presentation of user interfacesand display content, including one or more speakers and/or one or morevisual displays (e.g., a display panel located near to a driver's righthand in right-hand-side operated vehicles typical in the U.S.).

The vehicle 102 includes a plurality of sensors 260 configured tocollect sensor data in a vehicle driving environment 100. The pluralityof sensors 260 include one or more of a GPS 262, a LiDAR scanner 264,one or more cameras 266, a RADAR sensor 268, an infrared sensor 270, oneor more ultrasonic sensors 272, a DSRC module 274, an INS 276 includingaccelerometers and gyroscopes, and an odometry sensor 278. The GPS 262localizes the vehicle 102 in Earth coordinates (e.g., using a latitudevalue and a longitude value) and can reach a first accuracy level lessthan 1 meter (e.g., 30 cm). The LiDAR scanner 264 uses light beams toestimate relative distances between the scanner 264 and a target object(e.g., another vehicle 102), and can reach a second accuracy levelbetter than the first accuracy level of the GPS 262. The cameras 266 areinstalled at different locations on the vehicle 102 to monitorsurroundings of the camera 266 from different perspectives. In somesituations, a camera 266 is installed facing the interior of the vehicle102 and configured to monitor the state of the driver of the vehicle102. The RADAR sensor 268 emits electromagnetic waves and collectsreflected waves to determine the speed and a distance of an object overwhich the waves are reflected. The infrared sensor 270 identifies andtracks objects in an infrared domain when lighting conditions are poor.The one or more ultrasonic sensors 272 are used to detect objects at ashort distance (e.g., to assist parking). The DSRC module 274 is used toexchange information with a road feature (e.g., a traffic light). TheINS 276 uses the accelerometers and gyroscopes to measure the position,the orientation, and the speed of the vehicle. The odometry sensor 278tracks the distance the vehicle 102 has travelled, (e.g., based on awheel speed). In some embodiments, based on the sensor data collected bythe plurality of sensors 260, the one or more processors 202 of thevehicle monitor its own vehicle state 282, the driver or passenger state284, states of adjacent vehicles 286, and road conditions 288 associatedwith a plurality of road features.

The vehicle 102 has a control system 290, including a steering control292, a braking control 294, a throttle control 296, a transmissioncontrol 298, signaling and lighting controls, and other controls. Insome embodiments, one or more actuators of the vehicle control system290 are automatically controlled based on the sensor data collected bythe plurality of sensors 260 (e.g., according to one or more of thevehicle state 282, the driver or passenger state 284, states of adjacentvehicles 286, and/or road conditions 288).

The memory 206 includes high-speed random access memory, such as DRAM,SRAM, DDR RAM, or other random access solid state memory devices. Insome embodiments, the memory includes non-volatile memory, such as oneor more magnetic disk storage devices, one or more optical disk storagedevices, one or more flash memory devices, or one or more othernon-volatile solid state storage devices. In some embodiments, thememory 206 includes one or more storage devices remotely located fromone or more processing units 202. The memory 206, or alternatively thenon-volatile the memory within the memory 206, includes a non-transitorycomputer readable storage medium. In some embodiments, the memory 206,or the non-transitory computer readable storage medium of the memory206, stores the following programs, modules, and data structures, or asubset or superset thereof:

-   -   an operating system 214, which includes procedures for handling        various basic system services and for performing hardware        dependent tasks;    -   a network communication module 216, which connects each vehicle        102 to other devices (e.g., another vehicle 102, a server 104,        or a client device 108) via one or more network interfaces        (wired or wireless) and one or more communication networks 110,        such as the Internet, other wide area networks, local area        networks, metropolitan area networks, and so on;    -   a user interface module 218, which enables presentation of        information (e.g., a graphical user interface for an application        224, widgets, websites and web pages thereof, audio content,        and/or video content) at the vehicle 102 via one or more output        devices 212 (e.g., displays or speakers);    -   an input processing module 220, which detects one or more user        inputs or interactions from one of the one or more input devices        210 and interprets the detected input or interaction;    -   a web browser module 222, which navigates, requests (e.g., via        HTTP), and displays websites and web pages thereof, including a        web interface for logging into a user account of a user        application 224 associated with the vehicle 102 or another        vehicle;    -   one or more user applications 224, which are executed at the        vehicle 102. The user applications 224 include a vehicle user        application that controls the vehicle 102 and enables users to        edit and review settings and data associated with the vehicle        102; a model training module 226, which trains a vehicle data        processing model 250. The model 250 includes at least one neural        network and is applied to process vehicle data (e.g., sensor        data and vehicle control data) of the vehicle 102;    -   a data processing module 228, which performs a plurality of        on-vehicle tasks, including, but not limited to, perception and        object analysis 230, vehicle localization and environment        mapping 232, vehicle drive control 234, vehicle drive planning        236, local operation monitoring 238, and vehicle driving        behavior monitoring 240;    -   a vehicle database 242, which stores vehicle data 112,        including:        -   device settings 243, including common device settings (e.g.,            service tier, device model, storage capacity, processing            capabilities, communication capabilities, and/or medical            procedure settings) of the vehicle 102;        -   user account information 244 for the one or more user            applications 224 (e.g., user names, security questions,            account history data, user preferences, and predefined            account settings);        -   network parameters 246 for the one or more communication            networks 110, (e.g., IP address, subnet mask, default            gateway, DNS server, and host name);        -   training data 248 for training the vehicle data processing            model 250;        -   vehicle data processing models 250 for processing vehicle            data 112. The vehicle data processing models 250 include a            vehicle driving behavior model 252 applied to determine            vehicle driving behaviors of the vehicle 102 and/or other            adjacent vehicles 102;        -   sensor data 254 captured or measured by the plurality of            sensors 260;        -   mapping and location data 256, which is determined from the            sensor data 254 to map the vehicle driving environment 100            and locations of the vehicle 102 in the environment 100; and        -   vehicle control data 258, which is automatically generated            by the vehicle 102 or manually input by the user via the            vehicle control system 290 to drive the vehicle 102.

Each of the above identified elements may be stored in one or more ofthe previously mentioned memory devices, and corresponds to a set ofinstructions for performing a function described above. The aboveidentified modules or programs (i.e., sets of instructions) need not beimplemented as separate software programs, procedures, modules or datastructures, and thus various subsets of these modules may be combined orotherwise re-arranged in various embodiments. In some embodiments, thememory 206 stores a subset of the modules and data structures identifiedabove. In some embodiments, the memory 206 stores additional modules anddata structures not described above.

FIG. 3 is a block diagram of a server 104 for monitoring and managingvehicles 102 in a vehicle driving environment (e.g., the environment 100in FIG. 1 ), in accordance with some embodiments. Examples of the server104 include, but are not limited to, a server computer, a desktopcomputer, a laptop computer, a tablet computer, or a mobile phone. Theserver 104 typically includes one or more processing units (CPUs) 302,one or more network interfaces 304, memory 306, and one or morecommunication buses 308 for interconnecting these components (sometimescalled a chipset). The server 104 includes one or more user interfacedevices. The user interface devices include one or more input devices310, which facilitate user input, such as a keyboard, a mouse, avoice-command input unit or microphone, a touch screen display, atouch-sensitive input pad, a gesture capturing camera, or other inputbuttons or controls. Furthermore, in some embodiments, the server 104uses a microphone and voice recognition or a camera and gesturerecognition to supplement or replace the keyboard. In some embodiments,the one or more input devices 310 include one or more cameras, scanners,or photo sensor units for capturing images, for example, of graphicserial codes printed on electronic devices. The server 104 also includesone or more output devices 312, which enable presentation of userinterfaces and display content, including one or more speakers and/orone or more visual displays.

The memory 306 includes high-speed random access memory, such as DRAM,SRAM, DDR RAM, or other random access solid state memory devices. Insome embodiments, the memory includes non-volatile memory, such as oneor more magnetic disk storage devices, one or more optical disk storagedevices, one or more flash memory devices, or one or more othernon-volatile solid state storage devices. In some embodiments, thememory 306 includes one or more storage devices remotely located fromone or more processing units 302. The memory 306, or alternatively thenon-volatile memory within memory 306, includes a non-transitorycomputer readable storage medium. In some embodiments, the memory 306,or the non-transitory computer readable storage medium of the memory306, stores the following programs, modules, and data structures, or asubset or superset thereof:

-   -   an operating system 314, which includes procedures for handling        various basic system services and for performing hardware        dependent tasks;    -   a network communication module 316, which connects the server        104 to other devices (e.g., vehicles 102, another server 104,        and/or client devices 108) via one or more network interfaces        (wired or wireless) and one or more communication networks 110,        such as the Internet, other wide area networks, local area        networks, metropolitan area networks, and so on;    -   a user interface module 318, which enables presentation of        information (e.g., a graphical user interface for user        application 324, widgets, websites and web pages thereof, audio        content, and/or video content) at the vehicle 102 via one or        more output devices 312 (e.g., displays or speakers);    -   an input processing module 320, which detects one or more user        inputs or interactions from one of the one or more input devices        310 and interprets the detected input or interaction;    -   a web browser module 322, which navigates, requests (e.g., via        HTTP), and displays websites and web pages thereof, including a        web interface for logging into a user account of a user        application 324;    -   one or more user applications 324, which are executed at the        server 104. The user applications 324 include a vehicle user        application that associates vehicles 102 with user accounts and        facilitates controlling the vehicles 102, and enables users to        edit and review settings and data associated with the vehicles        102;    -   a model training module 226, which trains a vehicle data        processing model 250. The model 250 includes at least one neural        network and is applied to process vehicle data (e.g., sensor        data and vehicle control data) of one or more vehicles 102;    -   a data processing module 228, which manages a multi-vehicle        operation monitoring platform 332 configured to collect vehicle        data 112 from a plurality of vehicles 102, monitor vehicle        operation, detect faults, provide driving solutions, and update        additional vehicle information 114 to individual vehicles 102 or        client devices 108. The data processing module 228 manages        vehicle data 112 for each individual vehicle 102 separately or        processes vehicle data 112 of multiple vehicles 102 jointly        (e.g., statistically, in the aggregate);    -   vehicle server data 340, including:        -   device settings 342, which include common device settings            (e.g., service tier, device model, storage capacity,            processing capabilities, communication capabilities, and/or            medical procedure settings) of the server 104;        -   user account information 344 for the one or more user            applications 324 (e.g., user names, security questions,            account history data, user preferences, and predefined            account settings);        -   network parameters 346 for the one or more communication            networks 110, (e.g., IP address, subnet mask, default            gateway, DNS server, and host name);        -   training data 248 for training the vehicle data processing            model 250;        -   vehicle data processing models 250 for processing vehicle            data. The vehicle data processing models 250 include a            vehicle driving behavior model 252 applied to determine            vehicle driving behaviors of the vehicle 102 or other            adjacent vehicles 102;        -   vehicle data 112, which is collected from a plurality of            vehicles 102 and includes sensor data 254, mapping and            location data 256, and vehicle control data 258; and        -   additional vehicle information 114, including vehicle            operation information, fault information, and/or driving            solution information, which are generated from the collected            vehicle data 112.

In some embodiments, the model training module 226 includes a trainingdata augmentation module 328 configured to synthesize training databased on a predefined dataset or collected sensor data of the vehicles102. In some embodiments, the predefined dataset is used with thesynthesized training data to train a vehicle data processing model 250.In some embodiments, the collected sensor data is used with thesynthesized training data to train a vehicle data processing model 250.In some embodiments, the synthesized training data is used independentlyto train a vehicle data processing model 250. By these means, thetraining data can be augmented conveniently, allowing the vehicle dataprocessing model 250 to be trained efficiently and offer a higheraccuracy level.

Each of the above identified elements may be stored in one or more ofthe previously mentioned memory devices, and corresponds to a set ofinstructions for performing a function described above. The aboveidentified modules or programs (i.e., sets of instructions) need not beimplemented as separate software programs, procedures, modules or datastructures, and thus various subsets of these modules may be combined orotherwise re-arranged in various embodiments. In some embodiments, thememory 306 stores a subset of the modules and data structures identifiedabove. In some embodiments, the memory 306 stores additional modules anddata structures not described above.

FIGS. 4, 5A, and 5B provide background on the machine learning systemsdescribed herein, which are helpful in understanding the details of theembodiments described from FIG. 6 onward.

FIG. 4 is a block diagram of a machine learning system 400 for trainingand applying vehicle data processing models 250 for facilitating drivingof a vehicle, in accordance with some embodiments. The machine learningsystem 400 includes a model training module 226 establishing one or morevehicle data processing models 250 and a data processing module 228 forprocessing vehicle data 112 using the vehicle data processing model 250.In some embodiments, both the model training module 226 (e.g., the modeltraining module 226 in FIG. 2 ) and the data processing module 228 arelocated within the vehicle 102, while a training data source 404provides training data 248 to the vehicle 102. In some embodiments, thetraining data source 404 is the data obtained from the vehicle 102itself, from a server 104, from storage 106, or from a another vehicleor vehicles 102. Alternatively, in some embodiments, the model trainingmodule 226 (e.g., the model training module 226 in FIG. 3 ) is locatedat a server 104, and the data processing module 228 is located in avehicle 102. The server 104 trains the data processing models 250 andprovides the trained models 250 to the vehicle 102 to process real-timevehicle data 112 detected by the vehicle 102. In some embodiments, thetraining data 248 provided by the training data source 404 include astandard dataset (e.g., a set of road images) widely used by engineersin the autonomous vehicle industry to train vehicle data processingmodels 250. In some embodiments, the training data 248 includes vehicledata 112 and/or additional vehicle information 114, which is collectedfrom one or more vehicles 102 that will apply the vehicle dataprocessing models 250 or collected from distinct vehicles 102 that willnot apply the vehicle data processing models 250. The vehicle data 112further includes one or more of sensor data 254, road mapping andlocation data 256, and control data 258. Further, in some embodiments, asubset of the training data 248 is modified to augment the training data248. The subset of modified training data is used in place of or jointlywith the subset of training data 248 to train the vehicle dataprocessing models 250.

In some embodiments, the model training module 226 includes a modeltraining engine 410, and a loss control module 412. Each vehicle dataprocessing model 250 is trained by the model training engine 410 toprocess corresponding vehicle data 112 to implement a respectiveon-vehicle task. The on-vehicle tasks include, but are not limited to,perception and object analysis 230, vehicle localization and environmentmapping 232, vehicle drive control 234, vehicle drive planning 236,local operation monitoring 238, and vehicle driving behavior monitoring240. Specifically, the model training engine 410 receives the trainingdata 248 corresponding to a vehicle data processing model 250 to betrained, and processes the training data to build the vehicle dataprocessing model 250. In some embodiments, during this process, the losscontrol module 412 monitors a loss function comparing the outputassociated with the respective training data item to a ground truth ofthe respective training data item. In these embodiments, the modeltraining engine 410 modifies the vehicle data processing models 250 toreduce the loss, until the loss function satisfies a loss criteria(e.g., a comparison result of the loss function is minimized or reducedbelow a loss threshold). The vehicle data processing models 250 arethereby trained and provided to the data processing module 228 of avehicle 102 to process real-time vehicle data 112 from the vehicle.

In some embodiments, the model training module 402 further includes adata pre-processing module 408 configured to pre-process the trainingdata 248 before the training data 248 is used by the model trainingengine 410 to train a vehicle data processing model 250. For example, animage pre-processing module 408 is configured to format road images inthe training data 248 into a predefined image format. For example, thepreprocessing module 408 may normalize the road images to a fixed size,resolution, or contrast level. In another example, an imagepre-processing module 408 extracts a region of interest (ROI)corresponding to a drivable area in each road image or separates contentof the drivable area into a distinct image.

In some embodiments, the model training module 226 uses supervisedlearning in which the training data 248 is labelled and includes adesired output for each training data item (also called the ground truthin some situations). In some embodiments, the desirable output islabelled manually by people or labelled automatically by the modeltraining model 226 before training. In some embodiments, the modeltraining module 226 uses unsupervised learning in which the trainingdata 248 is not labelled. The model training module 226 is configured toidentify previously undetected patterns in the training data 248 withoutpre-existing labels and with little or no human supervision.Additionally, in some embodiments, the model training module 226 usespartially supervised learning in which the training data is partiallylabelled.

In some embodiments, the data processing module 228 includes a datapre-processing module 414, a model-based processing module 416, and adata post-processing module 418. The data pre-processing modules 414pre-processes vehicle data 112 based on the type of the vehicle data112. In some embodiments, functions of the data pre-processing modules414 are consistent with those of the pre-processing module 408, andconvert the vehicle data 112 into a predefined data format that issuitable for the inputs of the model-based processing module 416. Themodel-based processing module 416 applies the trained vehicle dataprocessing model 250 provided by the model training module 226 toprocess the pre-processed vehicle data 112. In some embodiments, themodel-based processing module 416 also monitors an error indicator todetermine whether the vehicle data 112 has been properly processed inthe vehicle data processing model 250. In some embodiments, theprocessed vehicle data is further processed by the data post-processingmodule 418 to create a preferred format or to provide additional vehicleinformation 114 that can be derived from the processed vehicle data. Thedata processing module 228 uses the processed vehicle data to at leastpartially autonomously drive the vehicle 102 (e.g., at least partiallyautonomously). For example, the processed vehicle data includes vehiclecontrol instructions that are used by the vehicle control system 290 todrive the vehicle 102.

FIG. 5A is a structural diagram of an example neural network 500 appliedto process vehicle data in a vehicle data processing model 250, inaccordance with some embodiments, and FIG. 5B is an example node 520 inthe neural network 500, in accordance with some embodiments. It shouldbe noted that this description is used as an example only, and othertypes or configurations may be used to implement the embodimentsdescribed herein. The vehicle data processing model 250 is establishedbased on the neural network 500. A corresponding model-based processingmodule 416 applies the vehicle data processing model 250 including theneural network 500 to process vehicle data 112 that has been convertedto a predefined data format. The neural network 500 includes acollection of nodes 520 that are connected by links 512. Each node 520receives one or more node inputs 522 and applies a propagation function530 to generate a node output 524 from the one or more node inputs. Asthe node output 524 is provided via one or more links 512 to one or moreother nodes 520, a weight w associated with each link 512 is applied tothe node output 524. Likewise, the one or more node inputs 522 arecombined based on corresponding weights w₁, w₂, w₃, and w₄ according tothe propagation function 530. In an example, the propagation function530 is computed by applying a non-linear activation function 532 to alinear weighted combination 534 of the one or more node inputs 522.

The collection of nodes 520 is organized into layers in the neuralnetwork 500. In general, the layers include an input layer 502 forreceiving inputs, an output layer 506 for providing outputs, and one ormore hidden layers 504 (e.g., layers 504A and 504B) between the inputlayer 502 and the output layer 506. A deep neural network has more thanone hidden layer 504 between the input layer 502 and the output layer506. In the neural network 500, each layer is only connected with itsimmediately preceding and/or immediately following layer. In someembodiments, a layer is a “fully connected” layer because each node inthe layer is connected to every node in its immediately following layer.In some embodiments, a hidden layer 504 includes two or more nodes thatare connected to the same node in its immediately following layer fordown sampling or pooling the two or more nodes. In particular, maxpooling uses a maximum value of the two or more nodes in the layer forgenerating the node of the immediately following layer.

In some embodiments, a convolutional neural network (CNN) is applied ina vehicle data processing model 250 to process vehicle data (e.g., videoand image data captured by cameras 266 of a vehicle 102). The CNNemploys convolution operations and belongs to a class of deep neuralnetworks. The hidden layers 504 of the CNN include convolutional layers.Each node in a convolutional layer receives inputs from a receptive areaassociated with a previous layer (e.g., nine nodes). Each convolutionlayer uses a kernel to combine pixels in a respective area to generateoutputs. For example, the kernel may be to a 3×3 matrix includingweights applied to combine the pixels in the respective area surroundingeach pixel. Video or image data is pre-processed to a predefinedvideo/image format corresponding to the inputs of the CNN. In someembodiments, the pre-processed video or image data is abstracted by theCNN layers to form a respective feature map. In this way, video andimage data can be processed by the CNN for video and image recognitionor object detection.

In some embodiments, a recurrent neural network (RNN) is applied in thevehicle data processing model 250 to process vehicle data 112. Nodes insuccessive layers of the RNN follow a temporal sequence, such that theRNN exhibits a temporal dynamic behavior. In an example, each node 520of the RNN has a time-varying real-valued activation. It is noted thatin some embodiments, two or more types of vehicle data are processed bythe data processing module 228, and two or more types of neural networks(e.g., both a CNN and an RNN) are applied in the same vehicle dataprocessing model 250 to process the vehicle data jointly.

The training process is a process for calibrating all of the weights w₁for each layer of the neural network 500 using training data 248 that isprovided in the input layer 502. The training process typically includestwo steps, forward propagation and backward propagation, which arerepeated multiple times until a predefined convergence condition issatisfied. In the forward propagation, the set of weights for differentlayers are applied to the input data and intermediate results from theprevious layers. In the backward propagation, a margin of error of theoutput (e.g., a loss function) is measured (e.g., by a loss controlmodule 412), and the weights are adjusted accordingly to decrease theerror. The activation function 532 can be linear, rectified linear,sigmoidal, hyperbolic tangent, or other types. In some embodiments, anetwork bias term b is added to the sum of the weighted outputs 534 fromthe previous layer before the activation function 532 is applied. Thenetwork bias b provides a perturbation that helps the neural network 500avoid over fitting the training data. In some embodiments, the result ofthe training includes a network bias parameter b for each layer.

Drivable-Area Assisted Synthetic Image Patching

FIG. 6 is a flow diagram of an example process 600 for augmentingtraining images by overlaying an image of an object on a drivable areaof a road in an image 602, in accordance with some embodiments. Asexplained above, a vehicle 102 has a plurality of sensors 260 includingone or more cameras 266. When the vehicle 102 drives on a road, a camera266 facing forward captures a sequence of images of the road. In someembodiments, the images of the road are processed to identify one ormore road features on the road. For example, the system uses an objectdetection model to determine position, depth, or motion information ofone or more road features in the images. Such position, depth, or motioninformation can be further applied to create a three-dimensional (3D)map of a scene where the vehicle 102 drives and to locate a position ofa camera that captures a respective image in the scene. A corpus oftraining images 640 stores a plurality of training images applied totrain models (e.g., the object detection model). The models are appliedto process the images of the road and facilitate vehicle driving. Insome embodiments, the corpus of training images 640 include a pluralityof road images that were previously captured by vehicles 102. In someembodiments, the corpus training images include one or more images 604augmented from existing road images, and the existing road images may ormay not be included in the corpus training images.

The process 600 is implemented at a computer system (e.g., part of avehicle 102 or a server 104). The computer system obtains a first image602 of a road and identifies a drivable area 606 of the road within thefirst image 602. The drivable area 606 of the road is a portion of theroad on which a vehicle 102 can drive. The drivable area 606 of the roadis visible and not occluded by any vehicle 102 or other object on thefirst image 602. The drivable area 606 of the road includes a road area608 and a shoulder area 610 (e.g., areas 610A and 610B). In someembodiments, the road area 608 is defined by solid edge markings 612.Further, in some embodiments, the road area 608 is divided to aplurality of drive lanes by one or more broken lane markings 614. Insome embodiments, the drivable area 606 of the road has an edgeoverlapping a shoulder barrier structure 616, and a shoulder area 610Aof the road is located between the shoulder barrier structure 616 and asolid edge marking 612. In an example, a shoulder area 610B of the roadis located between two solid edge marking 612 (e.g., at a highway exit).

The computer system obtains an image of an object 618. In someembodiments, the object includes a vehicle 102, and an image of thevehicle 618A is extracted (622) from a drivable area 606 of a road in afirst road image 620A. In some embodiments, the object includes aplurality of vehicles 102 located at different depths of a second roadimage 620B. An image of the plurality of vehicles 618B is extracted(622) from a drivable area 606 of a road in the second road image 620B.In some embodiments, the object includes one or more traffic safetyobjects (e.g., a barrel and a delineator). Two images of traffic safetyobjects 618C and 618D are extracted (622) from a drivable area 606 of aroad in the third road image 620C. In some embodiments, each image of anobject 618 corresponds to one or more rectangular bounding boxes in thecorresponding road image 620 (e.g., in image 620A, 620B, or 620C). Theroad image 620 is cropped according to the one or more rectangularbounding boxes to generate the image of the object 618. Further, in someembodiments, a background portion of the image of the object 618 is madetransparent, while a foreground portion of the image of the object 618remains opaque, containing visual information concerning the object.

The computer system generates a second image 604 from the first image602 by overlaying the image of the object 618 over the drivable area 606(e.g., the road area 608 or the shoulder area 610) of the road in thefirst image 602. A first set of pixels corresponding to a bottom surfaceof the object are aligned on a z-axis with a second set of pixelscorresponding to a second location of the drivable area 606 of the roadin the first image 602, such that the first set of pixels of the imageof the object 618 are placed immediately adjacent to or overlap thesecond set of pixels of the drivable area 606 in the first image 602.The object lies (624A) at least partially inside the drivable area 606of the first image 602. In some situations, the object lies entirelywithin the drivable area 606, while in some situations, at least aportion of the object lies externally outside the drivable area 606 inthe first image 602. In some embodiments, one or more of a size, theorientation, the aspect ratio, the brightness level, the contrast level,and the pixel resolution of the image of the object 618 are adjusted(624B) to ensure a realistic effect and fit into the first image 602.For example, an image of a vehicle 102 is rotated slightly to align abody of the vehicle 102 with adjacent lane markings 614 of the drivablearea 606 in the first image. After the second image 604 is generatedfrom the first image 602, the second image 604 is added to the corpus oftraining images 640 to be used by a machine learning system to generatea model for facilitating driving of a vehicle 102, e.g., at leastpartially autonomously.

In some embodiments, the road image 620 from which the image of theobject 618 is extracted is distinct from the first image 602.Alternatively, in some embodiments, the road image 620 from which theimage of the object 618 is extracted is the first image 602. The imageof the object 618 is extracted from a first location of the drivablearea 606 of the road in the first image 602 and added to a secondlocation of the drivable area 606 of the road in the first image 602 togenerate the second image 604. The second location is different from thefirst location. When the image of the object 618 is added to the secondlocation, the image of the object 618 remains at the first location ofthe drivable area 606 of the road in the first image 602. In someembodiments, the second location is determined (626) based on a task ofthe model to be trained using the second image 604. In an example, thetask of the model is to control an ego vehicle 102 to respond to anobstacle vehicle cutting into the same lane as the ego vehicle 102. Thesecond location is substantially close to the camera 266 in acorresponding field of view and could be anywhere from an adjacent laneto the same lane of the ego vehicle 102. In another example, the task ofthe model is to control a truck 102T to respond to an obstacle vehicle102 parked in the shoulder area 610, and the second location is in theshoulder area 610. Upon adding to the first image 602, the obstaclevehicle 102 has a distance from a solid road marking 612 or overlaps thesolid edge marking 612 based on the task, so the distance is adjusted inaccordance with a requirement of the task.

In some embodiments, the corpus of training images 640 further includesthe first image 602 from which the second image 604 is generated, andboth the first and second images 602 and 604 are applied to train amodel 250 to facilitate vehicle driving. Alternatively, in someembodiments, the corpus of training images 640 does not include thefirst image 602 from which the second image 604 is generated. In someembodiments, the corpus of training images 640 further includes the roadimage 620 from which the image of the object 618 is generated, and boththe second and road images 604 and 620 are applied to train the modelfor facilitating vehicle driving. Alternatively, in some embodiments,the corpus of training images 640 does not include the road image 620.

In some embodiments, the computer system uses machine learning to train(660) the model using the corpus of training images 640, including thesecond image 604, and distributes the model to one or more vehicles 102,including a first vehicle 102T. In use, the model is configured toprocess road images captured by the first vehicle 102T to facilitatedriving the first vehicle (e.g., at least partially autonomously). Insome situations, the first vehicle 102T uses the model to process theroad image in real time (i.e., having a latency that is within a vehicletolerance) as the road images are captured by the first vehicle 102T.After data augmentation, the corpus of training images 640 includes alarge number of training images that are applied to train the modelthoroughly, allowing the model to provide accurate real-time data thatmakes it possible to drive the first vehicle 102T safely andautonomously on road.

FIGS. 7A-7C are three images 620 in which a drivable area 606 is markedand images of objects 618 (e.g., objects 618A, 618B, 618C, and 618D) areextracted, in accordance with some embodiments. The image of the object618 is extracted from a road image 620 (e.g., images 620A-620C).Referring to FIG. 7A, in some embodiments, the object includes a vehicle102, and the image of the vehicle 618A is extracted from a drivable area606 of a road in a first road image 620A. Alternatively, in somesituations, the object includes one or more objects 702 existing on ashoulder area 610 of the road, and the image of one or more objects 702is extracted from the drivable area 606 of the road in the first roadimage 620A. Referring to FIG. 7B, in some embodiments, the objectincludes a plurality of vehicles 102 located at different depths of asecond road image 620B. A single image of the plurality of vehicles isextracted from a drivable area 606 of a road in the second road image620B. The plurality of vehicles 618B is overlaid on a drivable area 606of a road in a first image 602 using the single image of the vehicles618B. Referring to FIG. 7C, in some embodiments, the object includes oneor more traffic safety objects (e.g., a barrel and a delineator), andthe image of the traffic safety object 618C or 618D is extracted from adrivable area 606 of a road in the third road image 620C.

In some embodiments, the image of the object 618 corresponds to one ormore rectangular bounding boxes in the road image 620. The road image620 is cropped according to the one or more rectangular bounding boxesto generate the image of the object 618. Further, in some embodiments, abackground portion of the image of the object 618 is made transparent,while a foreground portion of the image of the object 618 remainsopaque.

In some embodiments, a drivable area detection model is applied toidentify a shoulder area 610 in the road image 620 and generate asegmentation mask identifying one or more vehicles 102 in the road image620. In an example, the segmentation mask has the same resolution as theroad image 620, and includes a plurality of elements, each of whichindicates a class of a corresponding pixel of the road image 620. Inanother example, the segmentation mask has a lower resolution than theroad image 620, and includes a plurality of elements, each of whichindicates a class of a corresponding set of neighboring pixel of theroad image 620. In some embodiments, the class is one of: a vehicle, atraffic sign, a drivable area, a shoulder area, or other road feature.Based on the segmentation mask, a plurality of regions of pixels in theroad image 620 is classified as one or more vehicles 102, and anobstacle vehicle (e.g., the vehicle 618A in FIG. 7A) is selected fromthe one or more vehicles 102 identified by the segmentation mask as theobject 618. In some situations, the obstacle vehicle (e.g., the vehicle618A in FIG. 7A) is selected from the one or more vehicles 102 in theroad image 620 for creating more training images, in accordance with adetermination that the obstacle vehicle 102 is not occluded ortruncated. The image of the object 618 is extracted based on a portionof the segmentation mask identifying the selected obstacle vehicle.

FIG. 8A is a first image 602 having a drivable area 606 of a road markedwith a plurality of road markings, in accordance with some embodiments,and FIG. 8B is a second image 604 that is generated from the first image602. The second image includes an image of an obstacle vehicle 618, inaccordance with some embodiments. A computer system obtains the firstimage 602 of a road captured from the perspective of a camera 266mounted on a vehicle 102 (e.g., an ego vehicle measuring the environmentaround itself via a plurality of sensors 260 including the camera 266).The drivable area 606 of the road is identified within the first image602. The drivable area 606 of the road is the portion of the road onwhich a vehicle 102 can drive. The drivable area 606 of the road isvisible and not occluded by any vehicle or object on the first image602. The drivable area 606 of the road includes a road area 608 and ashoulder area 610. Referring to FIG. 6 , in some embodiments, the roadarea 608 is defined by solid edge markings 612 and divided to aplurality of drive lanes by one or more broken lane markings 614. Inmany situations, both the solid edge markings and broken lane markingsare painted on the road. In some embodiments, the drivable area 606 ofthe road is bound by an edge overlapping a shoulder barrier structure616, and the shoulder area 610 of the road is located between theshoulder barrier structure 616 and a solid edge marking 612 or betweentwo solid edge marking 612.

In some embodiments, a drivable area detection model is applied toidentify the drivable area 606 of the road (including a shoulder area610) in the first image 602 and to generate a segmentation maskidentifying the drivable area 606 or one or more road features in thefirst image 602. The segmentation mask includes a plurality of elements,each of which indicates the class of one or more pixels of the firstimage 602. In some embodiments, the class is one of: a vehicle 102, atraffic sign 810, a drivable area 606, a road area 608, a shoulder area610, an edge marking 612, a lane marking 614, a shoulder barrierstructure 616, or another road feature.

In some embodiments, each solid edge marking 612, broken lane marking614, or shoulder barrier structure 616 is recognized, and associatedwith a respective edge line 802, lane line 804, and shoulder line 806,respectively. The drivable area 606 of the road is bound by two edgelines 802 in the first and second images 602 and 604. Each of the edgeline 802, the lane line 804, and the shoulder line 806 is associatedwith a set of pixels of the first image 602 that is marked with therespective line 802, 804, or 806. The pixel locations of the edge lines802, lane lines 804, and shoulder lines 806 form the first ground truthassociated with the first image 602. The image of the object 618 (e.g.,an image of a vehicle 102) is added at a second location 820 of thedrivable area 606 of the road in the first image 602 to generate thesecond image 604 in FIG. 8B. In some embodiments, the size of the imageof the object 618 is maintained while it is overlaid at the secondlocation 820 of the drivable area 606. Alternatively, in someembodiments, a size of the image of the object 618 is scaled while it isoverlaid at the second location 820 of the drivable area 606

In some embodiments, after the image of the object 618 is added, thefirst ground truth associated with the first image 602 is updated togenerate second ground truth associated with the second image 604. Thesecond ground truth includes the second location 820 of the drivablearea 606 of the road where the image of the object 618 is added. Duringtraining, the computer system trains, by the machine learning system, amodel (e.g., a vehicle detection model) using the second image 604 andsecond ground truth in a supervised manner.

In some embodiments, the second ground truth further includes the firstground truth, specifying locations of one or more road features in thesecond image 604. The one or more road features include one or moresolid edge markings 612, broken lane markings 614, shoulder barrierstructures 616, traffic lights, traffic signs 810, and/or traffic safetyobjects (e.g., a cone, a delineator, a barrel, a flasher, or areflector). For example, in some situations, a traffic sign is held by aconstruction worker and shows “SLOW” or “STOP” as chosen by theconstruction worker. The first ground truth includes a label associatedwith the traffic sign captured in the first image 602.

Referring to FIGS. 8A and 8B, the image of the object 618 occludes aportion of the lane line 8040. The second ground truth associated withthe second image 604 includes the occluded portion of the lane line8040, independently of occlusion of the portion of the lane line 804.During training, the computer system trains the model to interpolate anoccluded portion of a corresponding broken lane marking 614 withreference to the second ground truth, including the occluded portion8040 of the lane line. Specifically, in some embodiments, the computersystem detects an occluded portion 8040 of the lane line in the secondimage 604 using the model and compares the detected portion 8040 of thelane line with the second ground truth. Weights of neural networks ofthe model are adjusted to match the detected portion 8040 of the laneline with the second ground truth. By these means, the model is trainedto recognize or interpolate the occluded portion 8040 of the lane lineaccurately, allowing the vehicle 102 to be controlled to drive on adrive lane safely.

In some embodiments, the drivable area detection model is applied torecognize the drivable area 606 of the road, a road area 608, and ashoulder area 610. Further, in some embodiments, the drivable areadetection model is applied to identify one or more of solid edgemarkings 612, broken lane markings 614, and shoulder barrier structures616 and apply them to define the drivable area 606, road area 608, andshoulder area 610 of the road. The lines outputted by the drivable areadetection model include an edge line 802, lane line 804, and shoulderline 806 representing the solid edge marking 612, broken lane marking614, and shoulder barrier structure 616, respectively. Space between theedge line 802 and lane line 804 corresponds to a rightmost or leftmostdrive lane. Space between two edge lines 802 corresponds to a singlelane road or a shoulder area 610. Space between two lane lines 804corresponds to an intermediate lane. Space between the edge line 802 andshoulder line 806 corresponds to the shoulder area 610 of the road.

The edge line 802, lane line 804, and shoulder line 806 are distinctfrom each other. In some embodiments, each of the edge line 802, laneline 804, and shoulder line 806 corresponds to a respective distinctline color (e.g., red, green, and blue). Alternatively, in someembodiments, each of the edge line 802, lane line 804, and shoulder line806 corresponds to a respective distinct line style (e.g., solid,dashed, dotted). Alternatively, in some embodiments, each of the edgeline 802, lane line 804, and shoulder line 806 corresponds to arespective distinct line thickness. Alternatively, in some embodiments,every two of the edge line 802, lane line 804, and shoulder line 806 aredistinct in at least one of line color, style and thickness. Further, insome embodiments, an occluded line portion 902 is represented with adistinct line color, style or thickness from the edge line 802, laneline 804, and shoulder line 806.

FIG. 9A is a first image 910 having a plurality of vehicles 102 on adrivable area 606 of a road, in accordance with some embodiments. FIG.9B is a first diagram 920 of result lines recognized from the firstimage 910 using a drivable area detection model that is trained withoutsynthetic patching, and FIG. 9C is a second diagram 940 of result linesrecognized from the first image 910 using a drivable area detectionmodel that is trained with synthetic patching (i.e., with a second image604 that is augmented from a first image 602), in accordance with someembodiments. FIG. 9D is a second image 950 having a plurality ofvehicles 102 on a drivable area 606, in accordance with someembodiments. FIG. 9E is a third diagram 970 of result lines recognizedfrom the second image 950 using a drivable area detection model that istrained without synthetic patching, and FIG. 9F is a fourth diagram 990of result lines recognized from the second image 950 using a drivablearea detection model that is trained with synthetic patching (i.e., witha second image 604 that is augmented from a first image 602), inaccordance with some embodiments.

Referring to FIGS. 9B and 9E, a plurality of first line portions 902Aare occluded by vehicles 102 and recognized by machine learning usingthe drivable area detection model. The drivable area detection modelapplied to generate the lines does not involve synthetic patching. Thatis, the model was not trained with images 604 augmented by adding imagesof objects 618 on drivable areas 606 of roads in images 602. A pluralityof portions 904 of the drivable area 606 of the road are occluded byvehicles 102. The drivable area detection model fails to identify anedge line 802, a lane line 804, or a shoulder line 806 in each of theplurality of portions 904 of the drivable area 606. Referring to FIGS.9C and 9F, a drivable area detection model applied to generate the linesis trained using images involving synthetic patching, (i.e., usingimages 604 augmented by adding images of objects 618 on drivable areas606 of roads in images 602). While the plurality of portions 904 of thedrivable area 606 of the road are occluded by vehicles 102, the drivablearea detection model identifies one or more second line portions 902B ofthe edge line 802, the lane line 804, or the shoulder line 806 in eachof the plurality of portions 904 of the drivable area 606. Additionally,a shoulder line 906 and a plurality of third line portions 908 aremissing from FIGS. 9B and 9E, and can be identified using the drivablearea detection model that is trained with images involving syntheticpatching.

FIGS. 10A-10C are three images showing a process 1000 for adding one ormore images of vehicles 618 to a shoulder area 610 of a road in a firstimage 602, in accordance with some embodiments. The one or more imagesof vehicles 618 are extracted from the same first image 602 and added tothe shoulder area 610 of the road in the first image 602 to generate asecond image 604. A drivable area 606 of the road is identified in thefirst image 602 and includes the shoulder area 610. In some embodiments,a drivable area detection model is applied to identify the drivable area606 of the road (including the shoulder area 610) in the first image 602and generate a segmentation mask identifying the drivable area 606 orone or more road features in the first image 602. The segmentation maskincludes a plurality of elements, each of which indicates a class of oneor more pixels of the first image 602. For the one or more pixels of thefirst image 602, the class is one of: a vehicle 102, a drivable area606, a road area 608, a shoulder area 610, an edge marking 612, a lanemarking 614, a shoulder barrier structure 616, or other road feature.Sets of one or more pixels having the same class are adjacent to eachother, and combined to form a region, which is identified to beassociated with a corresponding road feature of the class. For example,four images of vehicles 618A-618D are identified on the drivable area606 of the road in the first image 602. Each of the images of vehicles618A-618D drives on a respective drive lane of the drivable area 606.Each vehicle image 618 has a respective depth measured from a camera 266that captures the first image 602, and is shown with a respective sizeon the first image 602.

Two of the four images of vehicles 618A-618D are selected and added tothe shoulder area 610. Referring to FIG. 10C, a vehicle image 618C′ iscopied from the vehicle image 618C located at a first location of thedrivable area 606 and added to a second location of the drivable area606 corresponding to the shoulder area 610. The first and secondlocations corresponding to the vehicle images 618C and 618C′ have thesame depths measured from the camera 266 that captures the first image602, and the images of vehicle 618C and 618C′ have the same size. Thevehicle image 618C′ is added to the second location of the drivable area606 in the first image 602 while the vehicle image 618C remains at thefirst location. Similarly, a vehicle image 618D′ is copied from thevehicle image 618D and added to the drivable area 606 corresponding tothe shoulder area 610. Locations corresponding to the vehicle images618D and 618D′ have the same depths measured from the camera 266, andthe images of vehicle 618D and 618D′ have the same size. The vehicleimage 618D′ is added to the drivable area 606 in the first image 602while the vehicle image 618D remains at its location. In someembodiments, the first image 602 is captured by a camera 266 facingforward to a driving direction of an ego vehicle 102, and a depth of avehicle is approximately measured by a vertical position 1002 on thefirst image 602.

In some embodiments not shown in FIGS. 10A-10C, the computer systemdetermines a first depth of field corresponding to the first location ofthe vehicle image 618C and a second depth of field corresponding to thesecond location of the vehicle image 618C′. The size of the vehicleimage 618C is scaled based on the ratio of the first and second depthsof field to generate the vehicle image 618C′. Alternatively, in someembodiments, the computer system determines vertical positions 1002corresponding to the first location of the vehicle image 618C and thesecond location of the vehicle image 618C′, and the size of the vehicleimage 618C is scaled based on a ratio of the vertical positions 1002 togenerate the vehicle image 618C′. The vehicle image 618C′ having thescaled size is overlaid at the second location of the drivable area 606,e.g., at a location 1004 on the shoulder area 610. In some embodiments,at least one of the orientation, the aspect ratio, the brightness level,the contrast level, or the pixel resolution of the vehicle image 618C isadjusted to generate the vehicle image 618C′ that is added to the firstimage 602.

FIG. 11A is an example image 604 showing that a vehicle image 618located on a road area 608 is copied and added to a shoulder area 610,in accordance with some embodiments, and FIG. 11B is another exampleimage 604 showing that one or more images of a vehicle 618 located on aroad area 608 are copied and added to the shoulder area 610, inaccordance with some embodiments. In accordance with the process 1000 asdescribed above with reference to FIGS. 10A-10C, the vehicle image 618is identified in a first image 602, copied or modified to a vehicleimage 618′, and added to the shoulder area 610 of a road in the firstimage 602 to generate a second image 604. Specifically, a deep learningmodel (e.g., a drivable area detection model) is applied to identify adrivable area 606 of the road (including a road area 608 and theshoulder area 610) and one or more road features (e.g., a vehicle 102)in the first image 602. The shoulder area 610 is defined between ashoulder barrier structure 616 and a solid edge marking 612. In someembodiments, a plurality of adjacent vehicle pixels are identified asbelonging to a class of vehicle by the deep learning model, and therebygrouped to form a corresponding vehicle image 618. In some embodiments,the vehicle image 618 has a rectangular shape and a background of thevehicle 102 is transparent in the vehicle image 618. The vehicle image618 is copied to another vehicle image 618′. In some embodiments, thesize of the vehicle image 618′ is adjusted based on a first depth of afirst location of the drivable area 606 where the vehicle image 618 isextracted and a second depth of a second location of the drivable area606 where the vehicle image 618′ will be added. In some embodiments, oneor more of the orientation, the aspect ratio, the brightness level, thecontrast level, and the pixel resolution of the vehicle image 618 areadjusted to generate the vehicle image 618′ to match imagecharacteristics at the second location of the drivable area 606. Thevehicle image 618′ is added to the second location of the drivable area606 (e.g., to the shoulder area 610).

Referring to FIG. 11A, the vehicle image 618 includes a single vehicle,and is added to the shoulder area 610 of the road in the first image602. Referring to FIG. 11B, an image of two vehicles 618 is separatelyextracted as two vehicles from a road area 608 of the road in the firstimage 602. Each of the two vehicle images is extracted from a firstlocation of the road area 608 and is added to a second location on theshoulder area 610 having the same depth as the first location. Statedanother way, vertical positions 1002 of the first and second locationsare consistent, while relative horizontal positions 1102 are swapped inthe two vehicle images 618′ compared with the two vehicle image 618.Alternatively, in some embodiments not shown, a single vehicle imageincludes more than one vehicle, and the vehicle image is extracted froma first image and added to a second image. The vehicles are adjusted andadded jointly while keeping their relative positions with respect toeach other.

In some embodiments, the vehicle image 618 occludes a portion of a roadfeature in the second image 604. The road feature is one of: a road area608, a shoulder area 610, a road marking 612 or 614 defining an ego lanein which the ego vehicle is driving, a road marking 612 or 614 definingan adjacent lane to the ego lane, an edge marking 612 defining ashoulder area, a road divider dividing the road, a traffic light, atraffic sign, or a temporary road marking defined by a plurality oftraffic safety objects. For example, the vehicle image 618 occludes aportion of a shoulder barrier structure 616 in FIGS. 11A and 11B and asignpost supporting a traffic sign 1106 in FIG. 11B. Additionally, theimage of the object 618 added to the first image 602 is not limited toan image of a vehicle 102. In some embodiments, the objects include oneor more road features that are commonly seen, such as a lane area, ashoulder area, an edge marking, a lane marking, a shoulder barrierstructure, a road divider, a traffic light, a traffic sign, apedestrian, a bicycle, or a traffic safety object. Alternatively, insome embodiments, the object is not commonly seen in the context ofvehicle driving. For example, an image of a deer, a chair, a cabinet, anairplane, a TV set, or a bear is added to the drivable area 606 of theroad of the first image 602 to generate the second image 604. The secondimage 604 is used to train a model that facilitates vehicle driving whenuncommonly seen objects appear on the drivable area 606 of the road.

FIG. 12 is a flow diagram of a process 1200 for adding an uncommonlyseen object 1202 onto an image 602, in accordance with some embodiments,and FIG. 13A-13E are five images 1300-1340 including distinct exampleimages of uncommonly seen objects added on a drivable area 606 of aroad, in accordance with some embodiments. The object 1202 is selectedfrom a plurality of known uncommon objects 1204 and marked with apredefined label that is used for all of the uncommon objects 1204. Insome embodiments, each of the plurality of known objects 1204 is markedwith the same predefined label (e.g., “uncommon object”). The pluralityof known uncommon objects 1204 is grouped in contrast with a set ofcommonly seen road features, such as a lane area, a shoulder area, anedge marking, a lane marking, a shoulder barrier structure, a roaddivider, a traffic light, a traffic sign, a pedestrian, a bicycle, or atraffic safety object. The plurality of known uncommon objects 1204includes objects that are not commonly seen on a drivable area 606 of aroad.

The image of the object 1202 is added to the first image 602 to generatethe second image 604. The second image 604 is added to a corpus oftraining images 640 used to train a model 250 that facilitates vehicledriving. In some embodiments, the model 250 is constructed based onone-class learning and trained using the corpus of training images 640to detect a first type of road features (e.g., road markings 612 and614) and a second type of outlier objects (e.g., the uncommonly seenobject 1202). The second type of outlier objects includes the pluralityof uncommonly-seen objects. The model 250 is trained to detect theuncommonly-seen objects to facilitate driving a vehicle 102 with atleast partial autonomy when one of the uncommonly-seen objects ispresent on the drivable area 606 in front of the vehicle 102.

In some embodiments, during training, a machine learning system of acomputer system (e.g., a model training module 226 of a server 104)trains the model 250 using the corpus of training images 640 byextracting a feature of the uncommon object 1202 in the second image 604using the model 250. The feature of the uncommon object 1202 is comparedwith second ground truth of the second image 604. The second groundtruth includes the predefined label of the uncommon object 1202. Weightsof the model 250 are adjusted to match the feature of the uncommonobject 1202 with the second ground truth. Specifically, the machinelearning system determines whether the extracted feature of the uncommonobject matches the second ground truth based on a weighted losscombining a descriptive loss 1208 and a compactness loss 1210. Thedescriptive loss 1208 indicates a distinction of the second type ofoutlier objects and the first type of road features, such as theaccuracy level of discriminating a patched object 1202 from a commonroad scene. The compactness loss 1210 is associated with the first typeof road features (e.g., road markings 612 and 614). The model 250 istrained to focus on differentiating different road featurerepresentations, rather than the second type of outlier objects (i.e.,the uncommon objects 1204). In some embodiments, referring to a clusterplot 1212, the road features are compact and substantially belong to onerepresentation 1214, thereby helping identify new uncommon objectfeatures that are outliers 1216 to the representation 1214.

Examples of the uncommonly seen object 1202 include a deer, a chair, acabinet, an airplane, a TV set, and a bear. Referring to FIG. 13A, animage of an airplane 1202A is added on a drivable area 606 in front oftraffic lights. Referring to FIG. 13B, an image of a cabinet 1202B isadded on or above a drivable area 606. It appears that the cabinet 1202Bjust fell off another vehicle 102 that drives in front of an ego vehicle102 from which a first image 602 is captured. In the image 1310, thecabinet is substantially close to the camera 266, blocks half of acorresponding field of view of the ego vehicle 102, and could hit afront window of the ego vehicle 102 at any time. Referring to FIG. 13C,an image of a deer 1202C is added on a drivable area 606 at night whenheadlights of the ego vehicle 102 illuminates the drivable area 606 foronly a few feet in front of the ego vehicle 102. The deer is exposed tothe illumination of the headlights, and has partially entered the roadarea 608 of the drivable area 606 of the road. Referring to FIG. 13D, animage of an elk 1202D is added on a drivable area 606, and partiallyoccludes a solid edge marking 612. Referring to FIG. 13E, an image of achair 1202E is added in the middle of a drive lane on a drivable area606, partially occluding a solid edge marking 612. Each of the image1300-1340 is used to train a model 250 to respond to an emergencysituation, so that the model 250 is configured to generate an outputthat enables a vehicle control instruction (e.g., an emergency stopcommand) and controls the ego vehicle 102 to address this emergencysituation (e.g., by slowing down the ego vehicle 102 and adjusting adriving direction to avoid hitting the object 1202).

FIG. 14 is a flow diagram of an example method 1400 for augmentingtraining data used for vehicle driving modelling, in accordance withsome embodiments. In some embodiments, the method 1400 is governed byinstructions that are stored in a non-transitory computer readablestorage medium and are executed by one or more processors of a computersystem (e.g., one or more processors 302 of a server 104). Each of theoperations shown in FIG. 14 may correspond to instructions stored in thecomputer memory or computer readable storage medium (e.g., the memory306 in FIG. 3 ) of the server 104. The computer readable storage mediummay include a magnetic or optical disk storage device, solid statestorage devices such as Flash memory, or other non-volatile memorydevice or devices. The computer readable instructions stored on thecomputer readable storage medium may include one or more of: sourcecode, assembly language code, object code, or other instruction formatthat is interpreted by one or more processors. Some operations in themethod 1400 may be combined and/or the order of some operations may bechanged.

The computer system obtains (1402) a first image 602 of a road andidentifies (1404) within the first image 602 a drivable area 606 of theroad. In some embodiments, the drivable area 606 of the road is aportion of the road on which a vehicle can drive, and is not occupied bya vehicle 102 or other objects. The drivable area 606 of the road isvisible and not occluded by any vehicle 102 or object on the first image602. In some embodiments, the drivable area 606 of the road includes ashoulder area 610 of the road. The computer system obtains (1406) animage of an object 618 (e.g., a vehicle), and generates (1408) a secondimage 604 from the first image 602 by overlaying the image of the object618 over the drivable area 606. The computer system adds (1410) thesecond image 604 to a corpus of training images 640 to be used by amachine learning system to generate a model 250 for facilitating drivingof a vehicle 102. In some situations, the model facilitates at leastpartial autonomously driving the vehicle. The model performs one or moreof a plurality of on-vehicle tasks including, but not limited to,perception and object analysis 230, vehicle localization and environmentmapping 232, vehicle drive control 234, vehicle drive planning 236, andlocal operation monitoring 238. In some embodiments, the corpus oftraining images 640 includes (1412) the first image 602. In someembodiments, the corpus of training images 640 includes the second image604, and does not include the first image 602.

In some embodiments, the computer system trains (1414) the model usingmachine learning. Training the model uses the corpus of training images640, including the second image 604 and distributes (1416) the model toone or more vehicles, including a first vehicle 102A. In use, the modelis configured to process road images captured by the first vehicle tofacilitate driving the first vehicle 102A (e.g., at least partiallyautonomously). In some embodiments, the model processes the road imagesin real time (i.e., having a latency that is within a vehicle tolerance)as the road images are captured by the first vehicle 102A. During thiscourse of real time image processing, the training data that areaugmented by the second image 604 helps the model provide accuratereal-time data that makes it possible to drive the first vehicle 102A atleast partially autonomously.

In some embodiments, the computer system generates the second image 604by extracting (1418) the image of the object 618 from the first image602 at a first location within the first image 602, selecting (1420) asecond location in the drivable area 606, and overlaying (1422) theimage of the object 618 at the second location of the drivable area 606.The image of the object 618 is retained at the first location, while itis duplicated to the second location. Specifically, a first set ofpixels corresponding to a bottom surface of the object are aligned on az-axis with a second set of pixels corresponding to the second locationof the drivable area 606 of the road, such that the first set of pixelsof the object is placed immediately adjacent to or overlap the secondset of pixels of the drivable area 606. In some embodiments, the firstand second locations are identified based on depths measured withreference to a camera location. Alternatively, in some embodiments, thefirst image 602 is divided to a plurality of rows and columns, and thefirst and second locations are identified based on a vertical (row)position 1002, a horizontal (column) position 1102, or both on the firstimage 602.

In some embodiments, the computer system maintains the size of the imageof the object 618 while overlaying the image of the object 618 at thesecond location of the drivable area 606. For example, the computersystem determines that the first location and the second location are atthe same depth of field in the second image 604, and the size of theimage of the object 618 remains the same in the second image 604 (see,e.g., FIGS. 11A and 11B). Alternatively, in some embodiments, thecomputer system determines a first depth of field corresponding to thefirst location and a second depth of field corresponding to the secondlocation. The size of the image of the object 618 is scaled based on aratio of the first and second depths of field, and the image of theobject 618 having a scaled size is overlaid at the second location ofthe drivable area 606 of the second image 604.

In some embodiments, the computer system generates the second image 604by obtaining (1424) the image of the object 618 from a source that isdistinct from the first image 602, selecting (1420) the second locationin the drivable area 606, and overlaying (1422) the image of the object618 at the second location of the drivable area 606.

In some embodiments, independently of a source of the image of theobject 618, the computer system adjusts one or more of: the size, theorientation, the aspect ratio, the brightness level, the contrast level,or the pixel resolution of the image of the object 618, before the imageof the object 618 is overlaid on the drivable area 606 of the road inthe first image 602 to generate the second image 604.

In some embodiments, the computer system obtains first ground truthassociated with the first image 602. After overlaying the image of theobject 618 on the drivable area 606, the computer system updates thefirst ground truth to generate a second ground truth associated with thesecond image 604. For example, the second ground truth includes thesecond location and other information of the object 618. The computersystem uses the machine learning system to train the model using thecorpus of training images 640, including the second image 604, in asupervised manner. Further, in some embodiments, the first ground truthspecifies at least locations of one or more road features in the firstimage 602, and the one or more road features include one or more of: avehicle 102, a bicycle, a pedestrian, a drivable area 606, a road area608, a shoulder area 610, an edge marking 612, a lane marking 614, aroad divider, a shoulder barrier structure 616, a traffic light, atraffic sign, or a traffic safety object. In some situations, the one ormore road features have some labels. For example, a traffic sign held bya construction worker may be labeled as “SLOW” or “STOP” as chosen bythe construction worker. The first ground truth includes a labelassociated with the traffic sign captured in the first image 602.

Additionally, in some embodiments, the object is a first object. In anexample, the first object is a vehicle 102 parked on a shoulder area610, immediately adjacent to an ego lane where the vehicle 102 isdriving. The first object occludes a portion of the one or more roadfeatures in the second image 604. The second ground truth associatedwith the second image 604 includes the same information of the firstground truth, independently of occlusion of the portion of the one ormore road features. More details about the impact of occlusion on thefirst ground truth are explained with reference to FIGS. 8 and 9A-9F.Further, in some embodiments, the computer system trains the model usingthe corpus of training images 640 by detecting the occluded portion ofthe one or more road features (e.g., part of a lane marker defining alane) in the second image 604 using the model, comparing the detectedportion of the one or more road features with the second ground truth,and adjusting the model to match the detected portion of the one or moreroad features with the second ground truth.

In some embodiments, the first image 602 is captured from a perspectiveof an ego vehicle 102, and the object includes a vehicle 102 that isdistinct from the ego vehicle 102. Further, in some embodiments, theobject occludes a portion of a road feature in the second image 604. Theroad feature is one of: a lane area, a shoulder area 610, a road markingdefining an ego lane in which the ego vehicle 102 is driving, a roadmarking defining an adjacent lane to the ego lane, an edge marking 612defining a shoulder area, a road divider dividing the road, a trafficlight, a traffic sign, or a temporary road marking defined by aplurality of traffic safety objects.

In some embodiments, the computer system applies a drivable areadetection model to identify a shoulder area 610 in the first image 602and generates a segmentation mask identifying one or more vehicles inthe first image 602. An obstacle vehicle is selected from the one ormore vehicles 102 identified by the segmentation mask as the object(e.g., when the obstacle vehicle is not occluded or truncated). Thecomputer system extracts the image of the object 618 based on a portionof the segmentation mask identifying the selected obstacle vehicle,selects a second location in the shoulder area 610, and overlays theimage of the object 618 at the second location. The image of theobstacle vehicle remains at a first location of the first image 602corresponding to the portion of the segmentation mask. Alternatively, insome embodiments, the computer system extracts the image of the object618 from another image distinct from the first image 602 using asegmentation mask. The computer system selects a second location in theshoulder area of the first image 602, and overlays the image of theobject at the second location of the first image 602 to generate thesecond image 604. More details on adding the image of the object on theshoulder area are explained above with reference to FIGS. 10A-10C and11A-11B.

In some embodiments, the object is an uncommon object 1202 that isselected from a plurality of known objects 1204. Further, in someembodiments, the uncommon object 1202 is marked (1428) with a predefinedlabel for the plurality of known objects 1204, and the predefined labelindicates that the known objects 1204 are not commonly seen on adrivable area 606 of a road. Stated another way, the plurality of knownobjects 1204 are uncommon in contrast to the plurality of road features(e.g., traffic signs, traffic lights, pedestrians, parked vehicles),which are commonly seen in a vehicle driving environment 100. In someembodiments, the model is trained to detect one or more of a pluralityof uncommonly-seen objects to facilitate driving the ego vehicle with atleast partial autonomy, and the plurality of uncommonly-seen objectsincludes the plurality of known objects 1204. In an example, the modeldoes not need to identify each uncommonly-seen object accurately.Rather, the model determines whether the uncommonly-seen object belongsto the plurality of uncommonly-seen objects. Examples of the pluralityof uncommonly-seen objects include, but are not limited to, an airplane1202A, a cabinet 1202B, a deer 1202C, an ELK 1202D, a chair 1202E, a TVset, and a bear.

Additionally, in some embodiments, the model is constructed (1430) basedon one-class learning and trained using the corpus of training images640, including the second image 604, to detect a first type of roadfeatures and a second type of outlier objects, and the second type ofoutlier objects include the plurality of uncommonly-seen objects.

Further, in some embodiments, during training, the computer systemextracts a feature of the uncommon object in the second image 604 usingthe model and compares the feature of the uncommon object with thesecond ground truth of the second image 604. The second ground truthincludes the predefined label. The computer system adjusts the weightsof the model and determines whether the extracted feature of theuncommon object matches the second ground truth based on a weighted losscombining a descriptive loss 1208 and a compactness loss 1210. Thedescriptive loss 1208 indicates the distinction between the second typeof outlier objects and the first type of road features. The compactnessloss 1210 is associated with the first type of road features. Moredetails about adding an uncommon object to a drivable area 606 of a roadin an image are explained above with reference to FIGS. 12 and 13A-13E.

It should be understood that the particular order in which theoperations in FIG. 14 have been described are merely exemplary and arenot intended to indicate that the described order is the only order inwhich the operations could be performed. One of ordinary skill in theart would recognize various ways to augmenting vehicle training data(e.g., related to a drivable area 606 of a road). Additionally, itshould be noted that details described with respect to FIGS. 1-13E and15-27 are also applicable in an analogous manner to the method 1400described above with respect to FIG. 14 . For brevity, these details arenot repeated here.

Background Augmentation Using Foreground Extraction for DriverMonitoring

Training images can be augmented and used to train a data processingmodel 250 for monitoring a driver or passenger of a vehicle 102accurately. In some embodiments, a vehicle 102 includes a camera 266facing an interior of the vehicle 102, and the camera 266 is configuredto capture images used to monitor the driver or a passenger sittinginside the vehicle 102. The model 250 is trained using training imagesand applied to process the images captured in real time by the camera266, thereby enabling the vehicle 102 to determine whether a vehicledriver is looking forward at a road or distracted. Distractions includelooking away from the front, closing one's eyes, or talking. A set oftraining images is captured from different camera angles to showdifferent drivers sitting in different vehicles 102. These trainingimages are oftentimes captured from the interior of the vehicles 102while the vehicles 102 are stationary, and therefore, have limitedvariations on background and lighting conditions. Such a set of trainingimages tends to overfit the data processing model 250 with limitedaccuracy. To address these issues, the set of training images isaugmented to include new training images by combining existing trainingimages with additional background images. The additional backgroundimages are captured directly by the cameras 266 mounted in the vehicles102 or provided by diverse image sources (e.g., an online imagedatabase). In general, the ground truth information of these newtraining images is automatically derived from that of the existingtraining images, and does not require human labelling. More importantly,the data processing model 250 is trained using the set of training data,including the new training images to monitor the driver or passengerreliably. Such a training data augmentation technique helps improve therobustness level of a corresponding driver monitoring system (DMS) asdriver backgrounds vary with different vehicles and under differentlighting conditions.

FIG. 15 is a flow diagram of an example process 1500 for augmenting atraining image 1502 by replacing a background image 1502B of thetraining image 1502, in accordance with some embodiments. The trainingimage 1502 is used to generate a model 250 for autonomously monitoringvehicle drivers or passengers. The training image 1502 includes thebackground image 1502B and a foreground driver image 1502F overlaid onthe background image 1502B. In some embodiments, the model 250 isapplied to autonomously monitor vehicle drivers to determine whether avehicle driver is looking forward at a road ahead of a vehicle 102. Insome embodiments, the model 250 is applied to monitor vehicle drivers todetermine whether a vehicle driver is looking forward at a road, lookingto the left, looking to the right, looking down, has closed eyes, or istalking. In this example, a vehicle driver shown in the foregrounddriver image 1502F is drinking from a bottle, and the model 250 isapplied to monitor whether the vehicle driver could possibly drinkalcohol. In some situations, in accordance with a determination that thechance of the vehicle driver drinking alcohol exceeds a thresholdprobability level, the vehicle 102 generates an alert message to thevehicle driver (or to a central hub).

Training data augmentation is implemented at a computer system (e.g., aserver 104). The computer system obtains an image 1502 of a first driverin an interior of a first vehicle 102 and separates (1504) theforeground driver image 1502F from the background image 1502B of theinterior of the first vehicle 102. The computer system obtains a secondbackground image 1506, and generates a second image 1508 by overlaying(1510) the foreground driver image 1502F onto the second backgroundimage 1506, e.g., at a position corresponding to a driver seat. Thesecond image 1508 is added to a corpus of training images 1520 to beused by a machine learning system to generate the model 250 forautonomously monitoring vehicle drivers. In some embodiments, the corpusof training images 1520 includes the image 1502, and the model 250 istrained by both the image 1502 and the second image 1508. Alternatively,in some embodiments, the corpus of training images 1520 does not includethe image 1502. The model 250 is trained by the second image 1508, whilethe image 1502 is not used to train the model 250.

Referring to FIG. 15 , in some embodiments, the second background image1506 includes an image of an interior of a second vehicle 102 that isdistinct from the first vehicle 102 captured in the background image1502B. The second background image 1506 is captured by a camera 266 thatfaces a driver of the second vehicle 102 in the interior of the secondvehicle 102. Alternatively, in some embodiments not shown, the secondbackground image 1506 does not include an image of an interior of avehicle 102. For example, the second background image 1506 includes anatural view to be captured as a convertible roof of a convertible caris open.

In some embodiments, prior to overlaying the driver image 1502F onto thesecond background image 1506, the computer system adjusts one or moreimage properties (e.g., brightness or contrast) of at least one of thedriver image 1502F and the second background image 1506 to matchlighting conditions of the driver image 1502F and the second backgroundimage 1506. For example, a first brightness level is determined for thebackground image 1502B, and a second brightness level is determined forthe second background image 1506. An image property scale factor isdetermined based on the first and second brightness levels, and appliedto scale the brightness level of the driver image 1502F before thedriver image 1502F is overlaid on the second background image 1506. Insome embodiments, the computer system normalizes at least one of thedriver image 1502F and the second background image 1506 to match averagebrightness levels of the driver image 1502F and the second backgroundimage 1506. In some embodiments, the computer system scales at least oneof the driver image 1502F and the second background image 1506. Forexample, the size of the driver image 1502F is enlarged before it isoverlaid on the second background image 1506. In some embodiments, thecomputer system adjusts the location of the driver image 1502F on thesecond background image 1506, e.g., on a driver seat or on a passengerseat. Further, in some embodiments, a combination of a subset of the oneor more image properties, the image size, and the driver image locationis adjusted for one of the driver image 1502F and the second backgroundimage 1506. In some embodiments, both the driver image 1502F and thesecond background image 1506 are adjusted, while the same property ordifferent properties are adjusted for the driver image 1502F and thesecond background image 1506. For example, the size of the driver image1502F is adjusted, and the contrast level of the second background image1506 is adjusted, so that the driver image 1502F and the secondbackground image 1506 are matched to each other in size and in lightconditions.

FIG. 16 is a flow diagram of an example process 1600 for separating aforeground driver image 1602F from a first image 1602 using asegmentation model 1604, in accordance with some embodiments. Thesegmentation model 1604 is applied to generate a segmentation mask 1606that associates a plurality of first pixels of the first image with thefirst driver image 1602F or a plurality of second pixels of the firstimage with a first background image 1602B. The first background image1602B is complementary to the foreground driver image 1602F, so theplurality of first pixels is complementary to the plurality of secondpixels. Stated another way, there is no single pixel belonging to boththe plurality of first pixels and the plurality of second pixels.

In some embodiments, the segmentation mask 1606 includes a plurality offirst elements 1606A corresponding to the plurality of first pixels ofthe driver image 1602F and a plurality of second elements 1606Bcorresponding to the plurality of second pixels of the first backgroundimage 1602B. In some embodiments, each element of the segmentation mask1606 represents a first probability of a corresponding pixel of thefirst image 1602 being a first pixel associated with the driver image1602F. For each element, when the first probability is greater than arespective threshold probability (e.g., 0.6), it is determined that thecorresponding pixel of the first image 1602 is associated with thedriver image 1602F. When the first probability is lower than or equal tothe respective threshold probability (e.g., 0.6), it is determined thatthe corresponding pixel of the first image 1602 is associated with thefirst background image 1602B. For example, an element 1610 of thesegmentation mask 1606 has a value of 0.3, indicating that theprobability of being associated with the driver image 1602F is 0.3. Theprobability of the corresponding pixel of the first image 1602 beingassociated with the first background image 1602B is therefore 0.7. Assuch, the element 1610 of the segmentation mask 1606 is associated withthe first background image 1602B.

Alternatively, in some embodiments not shown, each element of thesegmentation mask 1606 stores the probability of a corresponding pixelbeing associated with the first background image 1602B. For eachelement, when the second probability is greater than a respectivethreshold probability (e.g., 0.6), it is determined that thecorresponding pixel of the first image 1602 is associated with the firstbackground image 1602B, and when the second probability is lower than orequal to the respective threshold probability (e.g., 0.6), it isdetermined that the corresponding pixel of the first image 1602 isassociated with the driver image 1602F.

Additionally, in some embodiments, the segmentation mask 1606 has aresolution lower than that of the first image 1602. Each element of thesegmentation mask 1606 corresponds to a respective set of neighboringpixels (e.g., 3×3 pixels). In some embodiments, each element representsa first probability of a corresponding set of pixels of the first image1602 being associated with the driver image 1602F. For each element,when the first probability is greater than a respective thresholdprobability (e.g., 0.6), it is determined that the corresponding set ofpixels of the first image 1602 is associated with the driver image1602F. When the probability is lower than or equal to the respectivethreshold probability (e.g., 0.6), it is determined that thecorresponding set of pixels of the first image 1602 is associated withthe first background image 1602B. Alternatively, in some embodiments notshown, each element represents a second probability of a correspondingset of pixels of the first image 1602 being associated with the firstbackground image 1602B.

In some embodiments, each element of the segmentation mask 1606 is abinary probability, and is equal to one of two predefined values (e.g.,0 or 1). In some embodiments, each element of the segmentation mask 1606represents a first probability of a corresponding single pixel or acorresponding set of pixels of the first image 1602 being associatedwith the driver image 1602F. For example, the first elements 1606A areequal to 1, indicating that a corresponding first region of the firstimage 1602 is associated with the driver image 1602F. The secondelements 1606B are equal to 0, indicating that a corresponding secondregion of the first image 1602 is associated with the first backgroundimage 1602B. Alternatively, in some embodiments not shown, each elementof the segmentation mask 1606 represents a second probability of acorresponding single pixel or a corresponding set of pixels of the firstimage 1602 being associated with the first background image 1602B. Forexample, each element is equal to 1 or 0, indicating that acorresponding region of the first image 1602 is associated with thefirst background image 1602B or the driver image 1602F, respectively.

In some embodiments, the segmentation model 1604 is a U-Net 1608, whichis constructed based on a fully convolutional network. The U-Net 1608includes an encoder-decoder network having a series of encoding stages1612, a bottleneck network 1614 coupled to the series of encoding stages1612, and a series of decoding stages 1616 coupled to the bottlenecknetwork 1614. The series of decoding stages 1616 includes the samenumber of stages as the series of encoding stages 1612. In an example,the encoder-decoder network has four encoding stages 1612 and fourdecoding stages 1616. The bottleneck network 1614 is coupled between theencoding stages 1612 and decoding stages 1616. The first image 1602 issuccessively processed by the series of encoding stages 1612, thebottleneck network 1614, the series of decoding stages 1616, and apooling layer 1618 to generate the segmentation mask 1606.

The series of encoding stages 1612 includes an ordered sequence ofencoding stages 1612 and has an encoding scale factor. Each encodingstage 1612 applies successive Rectified Linear Units (ReLUs) to generatean encoded feature map having a feature resolution and a number ofencoding channels. Between every two encoding stages, the featureresolution is scaled down and the number of encoding channels is scaledup according to the encoding scale factor (e.g., using a max poolinglayer). The bottleneck network 1614 bridges the encoding and decodingstages, and includes successive ReLUs. The series of decoding stages1616 includes an ordered sequence of decoding stages 1616 and has adecoding upsampling factor. Each decoding stage 1616 applies successiveReLUs to generate a decoded feature map having a feature resolution anda number of decoding channels. Between every two decoding stages, thefeature resolution is scaled up and the number of decoding channels isscaled down according to the decoding upsampling factor (e.g., using anup conversion layer). Each encoding stage 1612 provides an encodedfeature map to a corresponding decoding stage 1616 via a skippedconnection, such that each decoding stage 1616 combines an input from acorresponding encoding stage 1612 with an input from a precedingdecoding stage 1616 or bottleneck network 1614.

FIG. 17 is a flow diagram of an example process 1700 for augmentingdriver images 1702, in accordance with some embodiments. The driverimages 1702 include a first driver image 1702A that is separated from afirst background image of a first image not shown in FIG. 17. In someembodiments, the first image is captured in an interior of a firstvehicle 102. Alternatively, in some embodiments, the first image isobtained for the purposes of providing the first driver image 1702A, andtherefore, captured in a scene distinct from the interior of the firstvehicle 102. A computer system obtains one or more background images1704, such as a first background image 1704A of an interior of a secondvehicle 102, a second background image 1704B of a starry night sky, anda third background image 1704C of a ground surface. Stated another way,each of the one or more background images 1704 is captured by a camera266 within or independently of an interior of a corresponding vehicle102. In some embodiments, one of the one or more background images 1704are extracted from an image database (e.g., downloaded from an onlineimage database, and may or may not include the interior of acorresponding vehicle 102).

The first driver image 1702A is overlaid on each of the one or morebackground images 1704 to form a corresponding second image 1706 (e.g.,images 1706A, 1706B, and 1706C). In some embodiments, during the courseof generating the corresponding second image 1706, at least one of thefirst driver image 1702A or respective background image 1704 is adjustedaccording to one or more image properties, normalized, or scaled in sizeto make the first driver image 1702A and respective background image1704 match each other.

In some embodiments, the driver images 1702 include a second driverimage 1702B of a second driver that is distinct from the driver of thefirst driver image 1702A. The second driver image 1702B is separatedfrom a respective background image of a third image not shown in FIG. 17. The second driver image 1702B is overlaid onto one of the one or morebackground images 1704 to generate a fourth image. The fourth image isalso added into the corpus of training images 248 to be used by amachine learning system to generate the model 250 for monitoring vehicledrivers. In some embodiments, two driver images 1702 and threebackground images 1704 are combined to generate at least six trainingimages. Further, in some embodiments, one or both of the first driverimage 1702A or respective background image 1704 is adjusted according toone or more image properties or relative location, normalized, or scaledin size to create a plurality of variations of the resulting trainingimages. By these means, the model 250 for monitoring vehicle drivers istrained using these training images to provide accurate driverinformation concerning driver states in a reliable manner when differentdrivers are driving different vehicles 102 under different lightingconditions.

FIG. 18 is a flow diagram of another example process 1800 for augmentingdriver images 1802, in accordance with some embodiments. The driverimages 1802 include a first driver image 1802A, a second driver image1802B, a third driver image 1802C, and a fourth driver image 1802D. Eachof the driver images 1802 is separated from a respective backgroundimage in a respective image 1804. For example, each driver image 1802A,1802B, 1802C, or 1802D is separated from the respective background imagein the respective image 1804A, 1804B, 1804C, or 1804D, respectively. Insome embodiments, each of the driver images 1802 is associated with adistinct driver. In some embodiments, a subset of the driver images 1802is associated with the same driver having different driver states (e.g.,action, location, or direction of view). In an example, referring toFIG. 18 , the four driver images 1802 capture four distinct drivers. Thefirst driver image 1802A shows a driver looking to the right and facingaway from a camera 266. The second driver image 1802B shows a driver whowears a face mask covering his nose and mouth and is looking slightlytowards his left shoulder. The third driver image 1802C shows a driverlooking down (e.g., at a mobile phone or at a control panel of hisvehicle 102), and the fourth driver image 1802D shows a driver lookingslightly to the right.

Each of the driver images 1802 is overlaid on a respective backgroundimage to generate a respective training image 1806. In some embodiments,each of the background images is distinct. In some embodiments, a subsetof the background images is identical, while the driver images 1802 tobe combined with the subset are distinct from each other. In thisexample, the first training image 1806A corresponds to a firstbackground image, and the training images 1806B-1806D correspond to thesame background image that is distinct from the first background imageused in the first training image 1806A. Although the training images1806B-1806D correspond to the same background image, the driver images1802B, 1802C, and 1802D are overlaid at different relative locations ofthe same background image. For example, the driver images 1802B and1802C are overlaid on a passenger seat of the background image of thetraining images 1806B and 1806C. The driver image 1802D is overlaid on adriver seat of the background image of the training image 1806D. Thefirst driver image 1802A is overlaid on a passenger seat of thebackground image of the training image 1806A. It is noted that thesteering wheel is located on the left side of a vehicle in variousembodiments of this application (e.g., the standard in the UnitedStates).

In some embodiments, the model 250 for monitoring vehicle drivers istrained to determine whether each driver image 1802 is associated with adriver or a passenger (e.g., based on the location on which therespective driver image 1802 is overlaid with respect to thecorresponding background image). The model 250 is further trained todetermine whether a person in each driver image 1802 is distracted andwhether to issue an alert message. This is in accordance with adetermination of whether a driver image 1802 is associated with a driveror a passenger. For example, the model 250 is trained to classify aperson in the training image 1806A or 1806C as a distracted passengerwithout issuing any alert message. However, when the person in thetraining image 1806A or 1806C is placed on the driver seat, the model250 is trained to classify the person in the training image 1806A or1806C as a distracted driver and enables issuing of an alert message.From a different perspective, in some embodiments, a single driver image1802 and a single background image are combined based on differentrelative locations to generate a plurality of training images 1806.

The training images 1806A-1806D are added to a corpus of training images248 applied to train the model 250 for monitoring drivers or passengers.In some embodiments, the corpus of training images 248 further includesa subset or all of the images 1804 from which the driver images 1802 areextracted. In some embodiments, the corpus of training images 248includes a plurality of training images 1806 generated from the samedriver image 1802 and the same background image. Each of the pluralityof training images 1806 includes a distinct combination of imageproperties, sizes, scale factors, and relative locations of the samedriver image 1802 and the same background image.

FIG. 19 is a two-dimensional (2D) clustering plot 1900 showing anexample distribution of representations of a plurality of backgroundimages 1902, in accordance with some embodiments. A computer system(e.g., a server 104) collects the plurality of background images 1902,for example, by receiving a first subset of background images fromcameras 266 of vehicles 102 or retrieving a second subset of backgroundimages from an image database. Each of the plurality of backgroundimages 1902 is mapped onto a respective point in a multidimensionalspace having a distance metric d. In an example, the multidimensionalspace is represented by the 2D cluster plot 1900 having two axes, andthe two axes correspond to two properties (e.g., a contrast level and abrightness level) of each of the plurality of background images 1902.Each dot on the cluster plot 1900 represents a respective backgroundimage 1902 located on the clustering plot 1900 via a first axis valueand a second axis value. Two dots represent two distinct backgroundimages 1902 on the cluster plot 1900. In some embodiments, the distancebetween dots is a Euclidean distance determined based on the first andsecond axis values of the two dots. More generally, the points may beplaced in an n-dimensional space, with each of the n dimensionscorresponding to a respective image property.

The plurality of background images 1902 are clustered using the distancemetric d to form a plurality of image clusters 1904 (e.g., the cluster1904A). For each of the image clusters 1904, one or more backgroundimages 1906 are identified in the image cluster 1904 to be the mostdistant (e.g., from a centroid (or center) 1908, according to thedistance metric d). The computer system forms a set of candidatebackground images including the identified one or more most distantbackground images 1906 in each of the image clusters 1904. A secondbackground image (e.g., image 1506 in FIG. 15 , images 1704 in FIG. 17 )is selected from the set of candidate background images. The secondbackground image is combined with a driver image to generate a trainingimage that is added into a corpus of training images 248 for training amodel 250 for monitoring vehicle drivers.

In some embodiments, K-means clustering is applied to form the pluralityof image clusters 1904 from the plurality of background images 1902based on the distance metric d. In accordance with K-means clustering, acomputer system selects a positive integer number K and selects Kcluster centers (also called centroids) 1908. In an example not shown,the selected integer number K is equal to 1. In the example shown inFIG. 19 , the selected integer number K is equal to 5, and the clustercenters 1908A, 1908B, 1908C, 1908D, and 1908E are selected (e.g., atrandom locations to get started). For each of the plurality ofbackground images 1902, the computer system determines a distance of arespective background image 1902 from each of the cluster centers 1908(e.g., 1908A-1908E). The respective background image 1902 is assigned toa respective image cluster 1904 associated with a respective clustercenter 1908 to which the respective background image 1902 has a shortestdistance (i.e., the distance between the respective background image1902 and the respective cluster center 1908 of the respective imagecluster 1904 is shorter than any other distance of the respectivebackground image 1902 and a remaining cluster center 1908). For example,the distance between the background image 1906A and the cluster center1908A is shorter than any other distance between the background image1906A and the remaining cluster centers 1908B, 1908C, 1908D, or 1908E.Therefore, the background image 1906A is assigned to the image cluster1904A corresponding to the cluster center 1908A.

Further, in some embodiments, the computer system iteratively adjustseach of the K cluster centers 1908 based on positions of the backgroundimages 1902 assigned to a respective image cluster 1904 and reassignseach background image 1902 to a respective image cluster 1904 associatedwith a respective cluster center 1908 to which the respective backgroundimage 1902 has a shortest distance, until positions of the K clustercenters 1908 do not change on the clustering plot 1900 (e.g., untilchanges of the positions of the K cluster centers 1908 are within apredefined tolerance).

Stated another way, in some embodiments, the plurality of backgroundimages 1902 are collected and mapped onto respective points in themultidimensional space (e.g., the 2D clustering plot 1900) having thedistance metric d. In accordance with the distance metric d, theplurality of background images 1902 are clustered to form a plurality ofimage clusters 1904. For each of the plurality of background images1902, a respective distance is determined between the respectivebackground image 1902 and a corresponding cluster center 1908 of animage cluster 1904 to which the respective background image 1902belongs. The second background image is selected from the plurality ofbackground images based on the respective distance between the secondbackground image and the corresponding cluster center 1908. Further, insome embodiments, for each image cluster 1904, a respective subset ofcandidate images is selected in accordance with a determination that adistance of each candidate image and a respective cluster center 1908 isgreater than a threshold distance or in accordance with a determinationthat the distance is greater than corresponding distances of a thresholdpercentage of background images in the same image cluster 1904. Forexample, each candidate image is selected if a distance of the candidateimage and the respective cluster center 1908 is greater than thecorresponding distances of 95% of the background images in the sameimage cluster 1904. As such, the second background image is selectedfrom the candidate images (i.e., distant or remote background images1906 in each image cluster 1904) to augment the corpus of trainingimages 248 with a high diversity level. Because the model 250 formonitoring vehicle drivers is trained using such diverse training images248, the model 250 provides accurate monitoring results in a reliablemanner.

FIG. 20 is a flow diagram of an example method 2000 for augmentingtraining images used for generating a model 250 for monitoring vehicledrivers, in accordance with some embodiments. In some embodiments, themethod 2000 is governed by instructions that are stored in anon-transitory computer readable storage medium and are executed (2002)by one or more processors of a computer system (e.g., one or moreprocessors 302 of a server 104). Each of the operations shown in FIG. 20may correspond to instructions stored in the computer memory or computerreadable storage medium (e.g., the memory 306 in FIG. 3 ) of the server104. The computer readable storage medium may include a magnetic oroptical disk storage device, solid state storage devices such as Flashmemory, or other non-volatile memory device or devices. The computerreadable instructions stored on the computer readable storage medium mayinclude one or more of: source code, assembly language code, objectcode, or other instruction format that is interpreted by one or moreprocessors. Some operations in the method 2000 may be combined and/orthe order of some operations may be changed.

The computer system obtains (2004) a first image of a first driver in aninterior of a first vehicle and separates (2006), from the first image,a first driver image from a first background image of the interior ofthe first vehicle. The computer system obtains (2008) a secondbackground image and generates (2010) a second image by overlaying thefirst driver image onto the second background image. The computer systemadds (2012) the second image to a corpus of training images 248 to beused by a machine learning system to generate a model 250 forautonomously monitoring vehicle drivers. Referring to FIG. 15 , a driverimage 1502F is separated from a first background image 1502B andoverlaid onto a second background image 1506 to generate a second image1508. The second image 1508 is added to the corpus of training images248 to be used to generate the model 250.

In some embodiments, the model 250 is trained (2014) for autonomouslymonitoring vehicle drivers to determine whether a vehicle driver islooking forward at a road ahead of a vehicle 102. In some embodiments,the model 250 is trained (2016) for autonomously monitoring vehicledrivers to determine whether a vehicle driver is looking forward at aroad, looking to the left, looking to the right, looking down, closinghis/her eyes, or talking.

In some embodiments, referring to FIG. 16 , the computer system applies(2018) a segmentation model 1604 to generate a segmentation mask 1606that associates a plurality of first pixels with the first driver imageand associates a plurality of second pixels of the first image with thefirst background image. Further, in some embodiments, the segmentationmodel 1604 includes (2020) a U-Net 1608 that is based on a fullyconvolutional network.

In some embodiments, prior to overlaying the first driver image onto thesecond background image, the computer system performs (2022) one or moreof: (i) adjusting one or more image properties (e.g., the brightnesslevel or the contrast level) of at least one of the first driver imageand the second background image to match lighting conditions of thefirst driver image with the second background image, (ii) normalizing atleast one of the first driver image and the second background image tomatch average brightness levels of the first driver image and the secondbackground image, and (iii) scaling at least one of the first driverimage and the second background image.

In some embodiments, the second background image does not include animage of an interior of a vehicle. Alternatively, in some embodiments,the second background image includes an image of an interior of a secondvehicle. The second background image is captured by a camera 266 thatfaces a driver of the second vehicle in the interior of the secondvehicle. Further, in some embodiments, the computer system obtains athird image of a second driver, and the first and second drivers aredistinct from each other. The computer system processes the third imageto separate a second driver image from a third background image andgenerates a fourth image by overlaying the second driver image onto thesecond background image. The fourth image is added to the corpus oftraining images 248 to be used by the machine learning system togenerate the model for monitoring vehicle drivers.

In some embodiments, referring to FIG. 19 , the computer system collects(2024) a plurality of background images 1902 and maps (2026) each of thebackground images 1902 onto a respective point in a multidimensionalspace having a distance metric d. The plurality of background images1902 is clustered (2028) using the distance metric d to form a pluralityof image clusters 1904. For each of the image clusters 1904, thecomputer system identifies two or more background images 1906 in theimage cluster 1904 that are most distant from each other according tothe distance metric d and forms a set of candidate background imagesincluding the identified most distant background images 1906 in each ofthe image clusters 1904. The second background image is selected fromthe set of candidate background images. Further, in some embodiments,the computer system clusters the plurality of background images 1902 byselecting a positive integer number K (e.g., 5) and selecting K clustercenters. For each of the plurality of background images 1902, thecomputer system determines (2030) the distance of the respectivebackground image 1902 from each of the cluster centers 1908 and assignsthe respective background image 1902 to a respective image cluster 1904associated with a respective cluster center 1908 to which the respectivebackground image 1902 has a shortest distance.

Stated another way, in some embodiments, the computer system collects(2024) a plurality of background images 1902 and maps (2026) each of thebackground images 1902 onto a respective point in a multidimensionalspace having a distance metric d. The plurality of background images1902 is clustered (2028) using the distance metric d to form a pluralityof image clusters 1904. For each of the plurality of background images1902, the computer system determines (2030), on a clustering plot 1900,a respective distance between the respective background image 1902 and acorresponding cluster center 1908 of an image cluster 1904 to which therespective background image 1902 belongs. The second background image isselected from the plurality of background images 1902 based on therespective distance between the second background image and thecorresponding cluster center 1908.

In some embodiments, the first driver image is overlaid at a firstlocation of the second background image. The computer system generatesan alternative image by overlaying the first driver image at a secondposition of the second background image and adds the alternative imageinto the corpus of training images 248 jointly with the second image.

In some embodiments, the computer system trains the model 250 forautonomously monitoring vehicle drivers to determine whether a vehicledriver is sitting on a driver seat or a passenger seat and, inaccordance with a determination of whether the vehicle driver is sittingon the driver seat or a passenger seat, classify the vehicle driver as adistracted driver or a distracted passenger.

It should be understood that the particular order in which theoperations in FIG. 20 have been described are merely exemplary and arenot intended to indicate that the described order is the only order inwhich the operations could be performed. One of ordinary skill in theart would recognize various ways to augmenting training data (e.g.,related to driver or passenger monitoring). Additionally, it should benoted that details described with respect to FIGS. 1-19 and 21-27 arealso applicable in an analogous manner to the method 2000 describedabove with respect to FIG. 20 . For brevity, these details are notrepeated here.

Data Augmentation with Traffic Safety Object Based Lane Definition

Under some circumstances, removable traffic safety objects (e.g., roadcones) are placed on a drivable area of a road (e.g., where there isroad work) to guide traffic temporarily. Examples of a traffic safetyobject include, but are not limited to, a delineator post 2110-1, a cone2110-2, and a barrel 2110-3, as shown below in FIG. 22 . Vehicle dataprocessing models 250 (FIG. 2 ) are created and trained to recognizetraffic conditions that include such removable traffic safety objects.Images of real road conditions are captured by vehicle cameras 266 (FIG.2 ) and applied to train the vehicle data processing models 250 (FIG. 2). However, such images only cover a limited number of real roadconditions, and the vehicle data processing models 250 trained withthese images may not be used to generate models that accuratelyanticipate all road conditions including different arrangements ofremovable traffic safety objects. Some embodiments are augmented toinclude images of traffic safety objects. The images of the trafficsafety objects are optionally extracted from a database of arbitraryobject primitives and overlaid on a drivable area of a road image togenerate augmented training images. The augmented training imagesrepresent realistic detour lanes that can be used train autonomousvehicle driving models. In some embodiments, the augmented trainingimages are used to create a scalable object-guided lane dataset (e.g.,including a corpus of training images), which is leveraged to train avehicle data processing model 250 (FIG. 2 ) to recognize object-guideddrive lanes (e.g., detour lanes) in real road images captured byvehicles 102 (FIG. 1 ).

Object-guided drive lanes are distinct from permanent drive lanes, e.g.,those defined by solid and dashed lane markings marked on a drivablearea of a road. The object-guided drive lanes are used to guide traffictemporarily. In some embodiments, an object-guided drive lane is definedby a single line of traffic safety objects. In some embodiments, anobject-guided drive lane is defined jointly by a line of traffic safetyobjects and a solid or dashed lane marking existing on the drivablearea. In some embodiments, an object-guided drive lane is defined by twosubstantially parallel lines of traffic safety objects. In someembodiments, each line of traffic safety objects consists of a singletype of traffic safety object. In some embodiments, each line of trafficsafety objects includes more than one type of traffic safety object,e.g., a mix of delineator posts 2110-1 and cones 2110-2 (FIG. 23 ).

Different object-guided drive lanes exhibit different characteristicsand have different lane curvatures, lane widths, object spacings, objecttypes, or lighting conditions. At the time of driving, a data processingmodule 228 (FIG. 2 ) of a vehicle 102 has to accurately detect thedifferent characteristics of the object-guided drive lanes because thedifferent characteristics determine a driving path of the vehicle 102. Amodel applied by the data processing module 228 is preferably trainedwith training images showing different road conditions, so that whenapplied by the data processing module 228, the model recognizes areal-time input image to facilitate at least partially autonomousdriving of the vehicle 102. However, due to the rarity of some roadconditions, it is difficult to locale images showing rare roadconditions, such as images including object-guided drive lanes. Invarious embodiments of this application, existing images are augmentedwith images of traffic safety objects to provide images withobject-guided drive lanes having different characteristics, therebyproviding an efficient solution to generate training images havingdifferent object-guided drive lanes. After being trained with thesetraining images, the model provides accurate road information tofacilitate at least partially autonomous driving of the vehicle 102.

FIG. 21 is an example training image 2100 showing a drivable area 2102of a road onto which copies of an image of a traffic safety object 2110are placed, in accordance with some embodiments. The training image 2100is generated from a first image after the copies of the image of thetraffic safety object 2110 are adaptively overlaid on a plurality ofpositions of the first image along a detour lane line 2104. In otherwords, the training image 2100 is a combination of the first image andthe copies of the image of the traffic safety object 2110. The detourlane line 2104 is applied to generate the training image 2100 from thefirst image and used jointly with the training data 2100 as a groundtruth. The detour lane line 2104 is not shown in the first image and thetraining image 2100, but is labeled data associated with the trainingimage 2100. In some embodiments, the image of the traffic safety object2110 has a transparent background so that it can be overlaid on thefirst image when a copy of the traffic safety object 2110 is placed ontothe drivable area 2102 of the road in the first image 2100. In someembodiments, the image of the traffic safety object 2110 is extractedfrom an existing portion 2106 of the same first image including theimage of the traffic safety object 2110. In some embodiments, the imageof the traffic safety object 2110 is extracted from a distinct imageincluding the image of the traffic safety object 2110. In someembodiments, the image of the traffic safety object 2110 is obtainedfrom a database, independently of whether the first image includes theimage of the traffic safety object 2110.

The first image includes a plurality of road features, such as a lanearea 2108, a vehicle 102, shoulder areas 2114, edge markings 2116, lanemarkings 2118, a shoulder barrier structure 2120, and a road sign 2122.In some embodiments not shown, the plurality of road features furtherincludes one or more of a road divider, a traffic light, a traffic sign,and a pedestrian and a bicycle. The positions of the traffic safetyobject 2110 are determined based on information for the plurality ofroad features.

Referring to FIG. 21 , the lane area 2108 is divided to four lanes2108A, 2108B, 2108C, and 2108D by three lane markings 2118. The detourlane line 2104 starts from a first lane 2108A and cuts through a secondlane 2108B that is immediately adjacent to the first lane 2108A. Thecopies of the image of the traffic safety object 2110 are placed alongthe detour lane line 2104 (which is temporarily added on the firstimage), thereby creating a road condition in which a sequence of trafficsafety objects 2110A-2110E are distributed in the first and second lanes2108A and 2108B. The sequence of traffic safety objects 2110A-2110E areconfigured to guide traffic on the drivable area 2102 towards third andfourth lanes 2108C and 2108D. In some embodiments, the training image2100 is applied to train and generate a model for facilitating at leastpartial autonomous driving of a vehicle 102 on roads with traffic safetyobjects. In an example, the detour lane line 2104 is a ground truthassociated with the training image 2100. The model includes a pluralityof neural network layers associated with a plurality of weights. Themodel is trained using the training image 2100 to derive the detour lanelines 2104 from a sequence of traffic safety objects. Weights of themodel are adjusted to match an output lane line recognized by the modelas a detour lane line, e.g., within a tolerance.

The training image 2100 augments from the first image, rather than beingcaptured from a real road condition. The first image is optionallycaptured by a camera 266 of a vehicle, extracted from a database, ormodified from another image. In some embodiments, the training image2100 is applied to train the model, and the first image that does notinclude a sequence of traffic safety objects, is not applied to trainthe model. The model is trained based on one or more augmented images.Alternatively, in some embodiments, both the first image and thetraining image 2100 are applied to train the model for facilitating atleast partial autonomous driving of a vehicle 102. The model isoptionally trained based on a combination of real and augmented images.

FIG. 22 is a flow diagram of an example process 2200 for augmentingtraining images with traffic safety objects and training a model 2206using the training images, in accordance with some embodiments. Theprocess 2200 is implemented by a training data augmentation module 328(FIG. 3 ) of a model training module of a server 104 (e.g., in FIG. 3 ).Specifically, the training data augmentation module 328 includes anobject-guided lane generator 2208, which further includes or is coupledto an extendable library 2210 of traffic safety objects 2110. Theobject-guided lane generator 2208 obtains a first image 2212 (e.g., froma corpus 2202 of training images) and obtains an image of a trafficsafety object 2110 from the library 2210. The object-guided lanegenerator 2208 further determines a detour path 2310 on a drivable areaon the first image 2212 and positions of plurality of traffic safetyobjects 2110 to be placed adjacent to the detour path 2310. Theobject-guided lane generator 2208 generates a second image 2214 (e.g.,the training image 2100 in FIG. 21 ) from the first image 2212 byadaptively overlaying a respective copy of the image of the trafficsafety object 2110 at each of the determined positions.

The second image 2214 is added to the corpus 2202 of training images tobe used by a machine learning system to generate a model 2206 forfacilitating at least partial autonomous driving of a vehicle 102. Insome embodiments, the corpus 2202 of training images includes a subsetof unlabeled images 2202A used for unsupervised training. In someembodiments, the corpus 2202 of training images includes a subset oflabeled images 2202B used for supervised training. For example, thesecond image 2214 is added into the corpus 2202 of training images withinformation of the detour path 2310 (e.g., a location of a detour laneline 2104 in FIG. 21 ), and the second image 2214 and the information ofthe detour path 2310 are applied jointly to train the model 2206 in asupervised manner. After the model 2206 is trained, the model 2206 isused to process unlabeled held-out data 2216A and/or labeled held-outdata 2216B and facilitate at least partially autonomous driving of afirst vehicle 102A.

The model 2206 includes a vehicle data processing model 250 (e.g., anautonomous vehicle driving model) for performing one or more of aplurality of on-vehicle tasks including, but not limited to, perceptionand object analysis 230, vehicle localization and environment mapping232, vehicle drive control 234, vehicle drive planning 236, and localoperation monitoring 238 in FIG. 2 . In some embodiments, the model 2206is trained using the corpus 2202 of training images, including thesecond image 2214, and distributed to one or more vehicles 102 includingthe first vehicle 102A. In use, the model 2206 is configured to processroad images captured by the first vehicle 102A, e.g., in real time, tofacilitate at least partially autonomously driving the first vehicle102A. In some situations, each of a subset or all of the road imagescaptured by the first vehicle 102A includes one or more traffic safetyobjects 2110. The model 2206 facilitates driving the first vehicle 102Aalong a detour path that is at least partially defined by a plurality oftraffic safety objects. Alternatively, in some situations, none of theimages captured by the first vehicle 102A includes any traffic safetyobject 2110.

In some embodiments, the object-guided lane generator 2208 obtains theimage of a traffic safety object 2110 from the extendable library 2210,applies a realistic effect onto the image of the traffic safety object2110, and overlays the image of the traffic safety object 2110 on thefirst image 2212 to generate the second image 2214. Specifically, insome embodiments, the object-guided lane generator 2208 applies therealistic effect by scaling a respective size of the respective copy ofthe image of the traffic safety object 2110 based on a respectiveposition where the respective traffic safety object 2110 is to beplaced, adjusting an orientation of the respective copy of the image ofthe traffic safety object 2110 based on a direction normal to thedrivable area 2102 at the respective position, and/or adjusting one ormore image properties (e.g., brightness, contrast) of the respectivecopy of the image of the traffic safety object 2110. Alternatively oradditionally, in some embodiments, the object-guided lane generator 2208adjusts one or more image properties (e.g., brightness, contrast) of thefirst image 2212 on which the respective copy of the image of thetraffic safety object 2110 is overlaid to match lighting conditions ofthe first image 2212 and the respective copy of the image of the trafficsafety object 2110.

In some embodiments, each type of traffic safety object 2110 correspondsto a primitive that is processed to enable the realistic effect of theimage of the traffic safety object 2110. For example, the image of thecone-based traffic safety object 2110-2 is generated from an originalimage having a cone structure. The cone structure is selected from theoriginal image using a snipping tool. The image of the cone-basedtraffic safety object 2110-2 is created in a standard image editingtool, and has a transparent background. The cone-based traffic safetyobject 2110-2 has a predefined cone height (e.g., 28 inch). The image ofthe cone-based traffic safety object 2110-2 is stored in the extendedlibrary 2210 with geometric information (e.g., the predefined coneheight).

In some embodiments, to ensure realism, the traffic safety object 2110appears to be part of a scene in the first image 2212. A base of thetraffic safety object 2110 on the copy of the image of the object 2110is aligned with, and overlaps, corresponding pixels of the drivable area2102 corresponding to a position where a corresponding copy of the imageof the traffic safety object 2110 is overlaid. Sizes of a sequence ofthe traffic safety objects 2110 (e.g., objects 2110A-2110E in FIG. 21 )are adjusted based on perceived depths of the traffic safety objects2110, optionally without explicitly knowing a depth of a scene where thetraffic safety objects 2110 are located.

Independently of whether the first image 2212 includes any trafficsafety object 2110, the first image 2212 is applied to generate thesecond image 2214 based on traffic safety objects 2110 stored in theextendable library 2210. This allows for the possibility of creating ancorpus 2202 of large amounts of training images from various real imagesof various road conditions. The resulting augmented corpus 2202 oftraining images can be further applied to train the model 2206 torecognize real-world object-guided drive lanes under complicated roadconditions and facilitate autonomous driving that takes into accountobject-guided drive lanes.

FIG. 23 is a flow diagram of an example process 2300 for augmentingtraining images by overlaying images of traffic safety objects 2110 on adrivable area 2102 of a road in an image, in accordance with someembodiments. The process 2300 for augmenting training images isimplemented at a computer system (e.g., a server 104 in FIGS. 1 and 3 ).The computer system obtains a first image 2212 and generates a secondimage 2214 from the first image 22012 by adding a plurality of trafficsafety objects 2110 on a drivable area 2102 of a road in the first image2212. For example, the plurality of traffic safety objects 2110 includeone or more delineator posts 2110-1, one or more cones 2110-2, one ormore barrels 2110-3, or a combination thereof. Referring to FIG. 23 , inthis example, the plurality of traffic safety objects 2110 includes aplurality of delineator posts 2110-1 (e.g., 6 unblocked delineator posts2110-1) in the second image 2214.

In accordance with the process 2300, the drivable area 2102 isidentified in the first image 2212, e.g., using a drivable area model2302. For example, all areas between curbs where are no objects areidentified as the drivable area 2102. In some embodiments, positions ofthe drivable area 2102 in a two-dimensional (2D) image coordinate systemof the first image 2212 are projected (2304) onto a three-dimensional(3D) camera coordinate system and a 3D inertial measurement unit (IMU)coordinate system (or any other reference plane/point on the vehicle102) successively. The computer system includes an object-guided lanegenerator 2208, which further includes or is coupled to an extendablelibrary 2210 of traffic safety objects 2110. The object-guided lanegenerator 2208 determines a detour path 2310 on the drivable area 2102of the road in the 3D IMU coordinate system (or other suitablecoordinate system) based on the projected positions of the drivable area2102 in the 3D IMU coordinate system. Positions 2312 for a plurality oftraffic safety objects 2110 are further identified on the drivable area2102 in the 3D IMU coordinate system. In some embodiments, the detourpath 2310 is defined by at least one detour lane line 2314 on thedrivable area 2102 of the road. In some embodiments, the positions 2312of the traffic safety objects 2110 in the 3D IMU coordinate system arefurther projected (2316) onto the 3D camera coordinate system and the 2Dimage coordinate system successively. For each of the plurality oftraffic safety objects 2110, a copy of an image of the respectivetraffic safety object 2110 is extracted from the extendable library2210, adaptively adjusted, and overlaid on the first image 2212 based onthe respective projected location in the 2D image coordinate system ofthe first image 2212. By these means, copies of the image of the trafficsafety object 2110 can be placed adjacent to the detour path 2310 on thesecond image 2214.

In some embodiments, the drivable area model 2302 is applied to identifythe drivable area 2102 of the road in the first image 2212. Asegmentation mask is generated to identify the drivable area 2102. Forexample, the segmentation mask includes a binary segmentation maskhaving a plurality of elements each of which indicates whether acorresponding pixel or region of pixels in the first image 2212correspond to the drivable area 2102 of the road. In some embodiments,the same segmentation mask, or a distinct segmentation mask, isgenerated to identify a plurality of road features in the first image2212. The corresponding segmentation mask has a plurality of elementseach of which indicates a class of one or more pixels of the first image2212. In an example, the class is optionally one of: a lane area 2108, avehicle 102, shoulder areas 2114, edge markings 2116, lane markings2118, a shoulder barrier structure 2120, a road sign 2122, a roaddivider, a traffic light, a traffic sign, and a pedestrian, and abicycle.

In some embodiments, the drivable area 2102 of the road includes an edgemarking 2116 and lane markings 2118 from a bird's eye view 2306 of theIMU coordinate system. These markings 2116 and 2118 define a pluralityof drive lanes on the drivable area 2102. The detour path 2310 isdefined by the plurality of traffic safety objects 2110 to besuperimposed on the plurality of drive lanes on the drivable area 2102.The detour path 2310 overrides the plurality of drive lanes on thedrivable area 2102 (e.g., changes a width of a middle drive lane in theview 2306). In some embodiments, the computer system executes a trainingdata augmentation application having a graphical user interface (GUI).The training data augmentation application is configured to display thefirst image 2212 or the bird's eye view 2306 on the GUI and receive auser input of at least one detour lane line 2314 to define the detourpath 2310 thereon. Alternatively, in some embodiments, the computersystem automatically generates the at least one detour lane line 2314based on a data augmentation scheme. The computer system determines oneor more object settings of: a total number of detour paths (NP), alength of the detour lane line 2314 (L), a number of objects 2110 on thedetour lane line 2314 (N), object spacings between each two immediatelyadjacent traffic safety objects 2110 (S_(i)), curvatures of the firstdetour lane line at the plurality of traffic safety objects (C_(i)), andrandomly generated deviations (also called jitters) from the detour laneline 2314. The positions of the plurality of traffic safety objects 2110on the detour lane line 2314 are determined based on these objectsettings.

The positions of these traffic safety objects 2110 are determined in aninertial measurement unit (IMU) coordinate system in a vehicle 102, andconverted to a position in the 3D camera coordinate system, and then toa position in the 2D image coordinate system. In some embodiments, thefirst image 2212 is obtained by the computer system with one or more ofcamera information, IMU information, information of camera-to-IMUtransformation, and information of IMU-to camera transformation. Thecamera information includes a camera intrinsic parameter K that isapplied to link coordinate values in the image coordinate system withcoordinate values in the camera coordinate system. For each imagecaptured by a camera 266 of an ego vehicle 102 (FIG. 2 ), the cameraintrinsic parameter K is determined in a pixel space that definesprojection of a 3D point into a 2D image plane. This corresponds to apinhole projection model defined for an undistorted image or needs tospecify distortion parameters, if the image is distorted. Examples ofthe camera intrinsic parameter K includes, but is not limited to, focallength, aperture, field-of-view, and resolution. For each image, thecamera-to-IMU transformation corresponds to a process of transformingthe 3D point in the camera coordinate system to the IMU coordinatesystem. The information of camera-to-IMU transformation is applied totransform a position of the 3D point in the camera coordinate system toa position in the IMU coordinate system. Conversely, the information ofIMU-to-camera transformation is applied to transform a position of a 3Dpoint in the IMU coordinate system to a position in the cameracoordinate system.

The extendable library 2210 stores information of a plurality of trafficsafety objects 2110 (e.g., a delineator post 2110-1, a cone 2110-2, abarrel 2110-3). The information of each traffic safety object 2110includes a physical height H_(TSO) of the respective traffic safetyobject 2110 in the real world. The object-guided lane generator 2208extracts an image of a traffic safety object 2110 from the extendedlibrary 2210 with related information (e.g., the physical heightH_(TSO)). The image of a traffic safety object 2110 has a differentperspective from that of the first image 2212. The object-guided lanegenerator 2208 determines a first scale of the extracted image of thetraffic safety object 2110 to align its perspective with the perspectiveof the first image 2212. In some embodiments, top-left and bottom-rightcorners of the traffic safety object 2110 are projected to [−0.5, −0.5,1]H_(TSO) and [0.5, 0.5, 1]H_(TSO), respectively. A bottom-middle pointof the traffic safety object 2110 is projected as [0, −0.5, 1]H_(TSO).As such, if the traffic safety object 2110 exists in the front of thecamera 266 (FIG. 2 ) capturing the first image 2212, the top-leftcorner, bottom-right corner, and bottom-middle point of the trafficsafety object 2110 are represented with the above 3D coordinate valuesto form a 3D scaled image of the traffic safety object 2110 in anobject-based coordinate system.

In some embodiments, the detour path 2310 is defined in the 3D IMU space(or any other suitable space) and corresponds to the positions of thetraffic safety objects 2110 located on a flat ground plane where thedrivable area 2102 of the road is located. The IMU has an approximateheight (IMU_height) from the drivable area of the road. In an example,the IMU coordinate system includes three orthogonal axes, an x-axisgoing forward, a y-axis going left, and a z-axis going up. A point ofthe drivable area 2102 is represented as a depth dx, an offset offset_y,and a curvature c, and a height Hz with respect to a location of theIMU. An example of a position on the drivable area 2102 correspond to adepth D_(TSO) and a height −IMU_height, and represented as [D_(TSO),offset_y±cD_(TSO) ², −IMU_height] in the IMU coordinate system. In someembodiments, a jitter is added in the depth and offset values to emulatereal-world not perfectly aligned placement of cones.

The 3D scaled image of the traffic safety object 2110 is transformedrigidly to the camera coordinate system to make the bottom-middle pointof the traffic safety object 2110 align with and overlap a point on thedrivable area 2102 (specifically, on the corresponding detour lane line2314 or position 2312 of the traffic safety object 2110). Each positionon the detour lane line 2314 in the IMU coordinate system is representedas P_(LANE). A 3D position of a bottom-middle point of the trafficsafety object 2110 in the IMU coordinate system is M×Xbm, where Xbm isthe 3D position of the bottom-middle point in the 3D camera coordinatesystem and M a camera-to-IMU conversion vector. In some embodiments, the3D position (M×Xbm) of the bottom-middle point of the traffic safetyobject 2110 in the IMU coordinate system overlaps the detour lane line(P_(LANE)), and the 3D position (M×Xbm) of the bottom-middle point ofthe traffic safety object 2110 in the IMU coordinate system is shiftedby an offset P_(LANE)−M×Xbm to the detour lane line (P_(LANE)). Cornersof the traffic safety object 2110 are translated using the offset(P_(LANE)−M×Xbm) to provide coordinate values of correspondingpositions. For example, a top-left corner, a bottom-right corner, and abottom-middle point correspond to positions X_(TL), X_(BR), and M×Xbm inthe IMU coordinate system, and are translated to X_(TL_LANE),X_(BR_LANE), and X_(BM_LANE), which are equal toX_(TL)+(P_(LANE)-M×Xbm), X_(BR)+(P_(LANE)−M×Xbm), and P_(LANE),respectively.

The image of the traffic safety object 2110 that is merged with thefirst image 2212 in the IMU coordinate system is further converted tothe camera coordinate system and the image coordinate systemsuccessively. The projected corners of the image of the traffic safetyobject 2110 provide a second scale and positions in a 2D image space.For example, a projection of the bottom middle point of the trafficsafety object 2110 is determined as K*inv(M)*X_(BM_LANE) on a 2D image,where K is the camera intrinsic parameter. Projections of the top leftcorner and the bottom right corner of the traffic safety object 2110 aredetermined as K*inv(M)*X_(TL_LANE) and K*inv(M)*X_(BR_LANE) on the 2Dimage, respectively.

Once the second scale and positions of the image of the traffic safetyobject 2110 are known, the image of the traffic safety object 2110 arecombined with the first image 2212 (e.g., using a weighted average in anHSV (hue, saturation, value) color space). The plurality of traffic laneobject 2110 are rendered with a decreasing depth (i.e. from far to near)to simulate a traffic safety object 2110 occluding another object ifneeded. Labels are generated, for example, to include the detour laneline 2314 connecting the bottom middle point of each traffic safetyobject 2110 in the first image. The labels are provided with the secondimage 2214 generated from the first image 2212 and applied as a groundtruth during a training process. In some situations, if a portion of abottom of the image of the traffic safety object 2110 needs to beprojected outside the drivable area 2102 of the road, the traffic safetyobject 2110 is not rendered and the corresponding detour lane line 2314is broken. In some embodiments, the plurality of traffic safety objects2110 include only one or two traffic safety object 2110, and arerejected and not rendered on the first image 2212.

Stated another way, in some embodiments, positions for a plurality oftraffic safety objects 2110 to be placed adjacent to the detour path2310 on the first image 2212 are based on alternative positions of theplurality of traffic safety objects 2110 in a first coordinate system(e.g., the 3D IMU coordinate system). The computer system obtains thecamera intrinsic parameter K and a conversion vector (e.g., M) betweenthe first coordinate system and a camera coordinate system. Based on thecamera intrinsic parameter K and the conversion vector, the alternativepositions of the plurality of traffic safety objects 2110 in the firstcoordinate system are converted to the positions of the plurality oftraffic safety objects 2110 in an image coordinate system.

In some embodiments, each traffic safety object 2110 has one or morereference nodes (e.g., a bottom-middle point, a bottom-right corner, atop-left corner). The computer system identifies the one or morereference nodes in the respective copy of the image of the trafficsafety object, and determines a respective location of each of the oneor more reference nodes in a first coordinate system (e.g., the 3D IMUcoordinate system) corresponding to a first space. The computer systemobtains the camera intrinsic parameter K and the conversion vector(e.g., M) between the first coordinate system and a camera coordinatesystem. Based on the camera intrinsic parameter K and the conversionvector M, the respective location of each of the one or more referencenodes in the first coordinate system is converted to a respectivelocation near a position of the respective traffic safety object 2110 inthe image coordinate system.

FIGS. 24A-24C are three example training images 2410, 2420, and 2430including a plurality of traffic safety objects 2110, in accordance withsome embodiments, and FIGS. 24D-24F are top views 2440, 2450, and 2460of a drivable area 2102 of a road in the training images in FIGS.24A-24C, in accordance with some embodiments. Each of the trainingimages 2410, 2420, and 2430 is generated from a first image including adrivable area 2102 of a road. The first image is captured during thedaytime. Each of the training images 2410, 2420, and 2430 includes adetour lane line 2314A, 2314B, or 2314C creating a distinct detour path2310A, 2310B, or 2310C on the same drivable area 2102 of the road in thefirst image. Additionally, in FIG. 24C, the detour lane line 2314Ccreates another distinct detour path 2310D jointly with a detour laneline 2314D. For each of the detour lane lines 2314A-2314D, copies of animage of a respective traffic safety object 2110 are adaptivelydistributed adjacent to the detour path 2310A, 2310B, 2310C, or 2310D.Each detour path 2310 is defined using the same type of traffic safetyobject 2110 (e.g., cones 2210-2 in FIG. 24A, barrels 2210-3 in FIG. 24B,and delineator posts 2210-1 in FIG. 24C). In some embodiments not shown,the plurality of traffic safety objects 2110 defining the detour path2310 include two or more types of traffic safety objects 2110.

Each of the training images 2410, 2420, and 2430 corresponds to arespective top view 2440, 2450, or 2460 of the drivable area 2102 of theroad in a 3D IMU coordinate system (also called a 3D IMU space). Thedetour lane line 2314A crosses a rightmost lane 2108 and a shoulder area2114. The detour lane line 2314B changes a width of a correspondingdrive lane to form the detour path 2310B. The detour lane lines 2314Cand 2314D change widths of two adjacent right drive lanes 2108 andredefine the detour lane 2310D to include part of the shoulder area 114.

In some embodiments, a server 104 (FIG. 3 ) of a computer systemincludes a training data augmentation module 328 in a model trainingmodule 326. The training data augmentation module 328 determines each ofthe detour lane lines 2314A-2314D that is adjacent to a respectivedetour path 2310 and configured to define the detour path 2310 on thedrivable area. Positions 2312 for one or more of the plurality oftraffic safety objects are identified on or near the respective detourlane line 2314. It is noted that a traffic safety object 2110 isoptionally disposed on a corresponding detour lane line 2314 or within adistance (e.g., <0.5 m) from the corresponding detour lane line 2314.

In some embodiments, the detour lane lines 2314A-2314D are not drawn onthe training images 2410, 2420, and 2430. Rather, information of thedetour lane lines 2314A-2314D or positions 2312 of the plurality oftraffic safety objects 2110 is stored with the training images as theground truth. During training, a model 2206 that facilitates vehicledriving is trained using the training images 2410, 2420, and 2430 andthe corresponding ground truth. Specifically, the computer systemiteratively recognizes, by a machine learning system, a lane line of thedetour path using the model 2206 and compares the recognized lane lineto a respective one of the detour lane lines 2314A-2314D of the groundtruth. The model 2206 is adjusted to match the recognized detour laneline to the respective one of the detour lane lines 2314A-2314D of theground truth. More specifically, weights of the model 2206 are adjustedto control a difference between the recognized detour lane line to therespective one of the detour lane lines 2314A-2314D of the ground truthwithin a tolerance.

In some situations, when copies of the image of the traffic safetyobject 2110 are placed on the drivable area 2102 of the road, thetraffic safety object 2110 occludes a portion of the drivable area 2102.Further, in some embodiments, the detour path 2310 or detour lane line2314 is adjacent to a road feature that is one of a vehicle 102, aperson, a bike, a motorcycle, a traffic sign, a road sign, etc.. A baseof the traffic safety object 2110 is posited on a visible portion of thedrivable area 2102. The road feature may be partially occluded by atraffic safety object 2110 based on depth values of the road feature andthe traffic safety object 2110, and the traffic safety object 2110 isoverlaid partially on the road feature. For example, referring to FIG.24B, the road feature includes a vehicle 102 that is partially occludedby an image of a barrel 2110T.

FIGS. 25A-25C are another three example training images 2510, 2520, and2530 including a plurality of traffic safety objects 2110, in accordancewith some embodiments, and FIGS. 25D-25F are top views 2540, 2550, and2560 of a drivable area 2102 of a road in the training images in FIGS.25A-25C, in accordance with some embodiments. Each of the trainingimages 2510, 2520, and 2530 is generated from a first image including adrivable area 2102 of a road. The first image is captured at night. Eachof the training images 2510, 2520, and 2530 includes a pair of detourlane lines 2314E, 2314F, or 2314G creating a distinct detour path 2310E,2310F, or 2310G on the same drivable area 2102 of the road in the firstimage. For each pair of detour lane lines 2314E, 2314F, or 2314G, copiesof an image of a respective traffic safety object 2110 are adaptivelydistributed adjacent to the detour path 2310E, 2310F, or 2310G. Eachdetour path 2310 is defined using the same type of traffic safety object2110 (e.g., cones 2210-2 in FIG. 25A and two different types ofdelineator posts 2210-1 in FIGS. 25B and 25C).

Each of the training images 2510, 2520, and 2530 corresponds to arespective top view 2540, 2550, or 2560 of the drivable area 2102 of theroad in a 3D IMU coordinate system (or other suitable coordinatesystem). While the detour path 2310E is substantially identical to thedetour path 2310F, a left detour lane line 2314E is slightly longer andhas more traffic safety objects 2110 than a left detour lane line 2314F,and a right detour lane line 2314E is slightly shorter and has lesstraffic safety objects 2110 than a right detour lane line 2314F. Thedetour path 2310G has an opposite direction or curvature to those of thedetour paths 2310E and 2310F. A corresponding left detour lane line2314G has more traffic safety objects 2110 (i.e., smaller objectspacings) than any other lane lines 2314E and 2314F.

Each of the detour paths 2310E-2310G is defined by two substantiallyparallel detour lane lines 2314E, 2314F, or 2314G, and positions 2312 ofone or more of the traffic safety objects 2110 are substantially on ornear the detour lane lines 2314E, 2314F, or 2314G. Specifically, foreach training image 2510, 2520, or 2530, the training data augmentationmodule 328 determines a first detour lane line 2314E, 2314F, or 2314Gthat is adjacent to a detour path 2310E, 2310F, or 2310G and configuredto define the detour path 2310E, 2310F, or 2310G on the drivable area2102. The training data augmentation module 328 further identifies thepositions 2312 for one or more of the plurality of traffic safetyobjects 2110 on or near the first detour lane line 2314E, 2314F, or2314F based on one or more object settings. Further, the training dataaugmentation module 328 (FIG. 3 ) determines a second detour lane line2314E, 2314F, or 2314F that is adjacent to the detour path 2310E, 2310F,or 2310G and configured to define the detour path 2310E, 2310F, or 2310Gon the drivable area 2102 jointly with the first detour lane line 2314E,2314F, or 2314F. The training data augmentation module 328 furtheridentifies the positions 2312 for one or more of the plurality oftraffic safety objects 2110 on or near the second detour lane line2314E, 2314F, or 2314F based on one or more object settings.Additionally, in some embodiments, the training data augmentation module328 (FIG. 3 ) converts the positions 2312 of the plurality of trafficsafety objects 2110 from the IMU coordinate system to the positions in acamera coordinate system. During this conversion, one or more referencenodes (e.g., a top-left corner, a bottom-right corner, and abottom-middle point) in the respective copy of the image of the trafficsafety object 2110 are used.

Referring to FIGS. 24A-24F and 25A-25C, different detour paths 2310 arevirtually created on a road image that has a drivable area 2102 havingone or more drive lanes or a shoulder area while not having any detourpath. A single first image is augmented to multiple training images thatcorrespond to complicated road conditions involving different types ofdetour paths 2310. As a result, engineers do not need to recreate thedifferent detour paths in real life and can train a vehicle dataprocessing model 250 efficiently and reliably using the augmentedtraining images.

FIGS. 26A-26F are six training images 2610-2660 showing a drivable area2102 of a road where copies of an image of a traffic safety object areplaced to define distinct detour paths 2310, in accordance with someembodiments. In some embodiments, each training image 2610-2660 isaugmented from a distinct first image. Alternatively, in someembodiments, two or more of the training images 2610-2660 are augmentedfrom the same first image, when different road features are added and/orwhen image characteristics are adaptively adjusted. For each trainingimage, copies of an image of a respective traffic safety object 2110 areadded to create one or more detour paths 2310.

Referring to FIGS. 26A and 26B, the training images 2610 and 2620 showroad conditions at night, and image properties of each copy of the imageof the traffic safety object 2110-2 are adjusted according to arespective local lighting condition. In some embodiments, the firstimage from which the training image 2610 or 2620 is generated iscaptured in the daytime. Image properties of the first image is adjustedand a lighting effect 2670 is added to the first image to create theroad conditions at night for the training image 2610 or 2620. Referringto FIGS. 26C and 26F, traffic safety objects 2110-2 are added alongthree or more detour lane lines 2314 on the first image, therebycreating complex routing patterns. Referring to FIG. 26D, the trainingimage 2640 shows a raining weather condition in which the drivable area2102 has a different contrast level. A front wiper blade 2672 appears ina field of view of the training image 2640. Copies of an image of adelineator post 2110-1 are aligned and placed along a detour lane linein the training image 2640. Referring to FIG. 26E, four copies of animage of a barrel 2110-3 are added adaptively in the first image togenerate the training image 2640.

FIG. 27 is a flow diagram of another example method 2700 for augmentingtraining data used for generating autonomous vehicle driving modelling,in accordance with some embodiments. In some embodiments, the method2700 is governed by instructions that are stored in a non-transitorycomputer readable storage medium and are executed by one or moreprocessors of a computer system (e.g., one or more processors 302 of aserver 104 in FIG. 3 ). Each of the operations shown in FIG. 27 maycorrespond to instructions stored in the computer memory or computerreadable storage medium (e.g., the memory 306 in FIG. 3 ) of the server104. The computer readable storage medium may include a magnetic oroptical disk storage device, solid state storage devices such as Flashmemory, or other non-volatile memory device or devices. The computerreadable instructions stored on the computer readable storage medium mayinclude one or more of: source code, assembly language code, objectcode, or other instruction format that is interpreted by one or moreprocessors. Some operations in the method 2700 may be combined and/orthe order of some operations may be changed.

The computer system obtains (2702) a first image 2212 of a road andidentifies (2704) within the first image 2212 a drivable area 2102 ofthe road. The computer system obtains (2706) an image of a trafficsafety object 2110 (e.g., a cone 2110-2, a delineator post 2110-1, abarrel 2110-3). In some embodiments, the image of the traffic safetyobject 2110 has a transparent background. In some embodiments, the imageof the traffic safety object 2110 is extracted from an alternative imageby removing a background of the alternative image. The computer systemdetermines a detour path 2310 on the drivable area 2102 (2708) andpositions for a plurality of traffic safety objects 2110 to be placedadjacent to the detour path 2310 (2710). The computer system generates(2712) a second image 2214 from the first image 2212 by adaptivelyoverlaying a respective copy of the image of the traffic safety object2110 at each of the determined positions. The second image 2214 is added(2714) to a corpus 2202 of training images to be used by a machinelearning system to generate a model 2206 (e.g., a vehicle dataprocessing model 250) for facilitating at least partial autonomousdriving of a vehicle.

In some embodiments, the computer system trains (2716), using machinelearning, the model 2206 using the corpus 2202 of training images,including the second image 2214. The model 2206 is distributed (2718) toone or more vehicles, including a first vehicle. In use, the model 2206is configured to process (2720) road images captured by the firstvehicle to facilitate at least partially autonomously driving the firstvehicle. For example, the model 2206 performs one or more of a pluralityof on-vehicle tasks including, but not limited to, perception and objectanalysis 230, vehicle localization and environment mapping 232, vehicledrive control 234, vehicle drive planning 236, and local operationmonitoring 238. In some situations, the model 2206 processes the roadimages in real time, and the road images optionally have one or moretraffic safety objects 2110 or do not have any traffic safety objects2110.

In some embodiments, the computer system adaptively overlays arespective copy of the image of the traffic safety object 2110 at eachof the determined positions by at least scaling (2722) a respective sizeof the respective copy of the image of the traffic safety object 2110based on a respective position where the respective traffic safetyobject 2110 is to be placed. In some embodiments, the computer systemadaptively overlays a respective copy of the image of the traffic safetyobject 2110 at each of the determined positions by at least adjusting(2724) an orientation of the respective copy of the image of the trafficsafety object 2110 based on a direction normal to the drivable area 2102at the respective position. In some embodiments, the computer systemadaptively overlays a respective copy of the image of the traffic safetyobject 2110 at each of the determined positions by at least adjusting(2726) one or more image properties of the respective copy of the imageof the traffic safety object 2110. In the above embodiments, therespective copy of the image of the traffic safety object 2110 isadjusted to match the first image 2212 on lighting conditions, abrightness level, a contrast level, relative sizes, relative positions.

In some embodiments, the computer system obtains information for aplurality of road features, including one or more of: a vehicle, a lanearea, a shoulder area, an edge marking, a lane marking, a shoulderbarrier structure, a road divider, a traffic light, a traffic sign, aroad sign, a pedestrian, and a bicycle. The determined positions aredetermined based on the information for the plurality of road features.

In some embodiments, the detour path 2310 is defined (2728) by twosubstantially parallel detour lane lines 2314, and positions of one ormore of the traffic safety objects 2110 are substantially on or near thedetour lane lines 2314. For example, each the one or more of the trafficobjects is within a predefined distance (e.g., less than 0.5 meter) fromthe detour lane lines 2314 to define the detour path 2310 properly.

In some embodiments, the computer system determines the detour path 2310on the drivable area 2102 by determining (2730) a first detour lane line(e.g., left detour lane lines 2314E, 2314F, and 2314G in FIG. 25D-27F)that is adjacent to the detour path 2310 and configured to define thedetour path 2310 on the drivable area 2102 and identify (2732) positionsfor one or more of the plurality of traffic safety objects 2110 on ornear the first detour lane line. In an example, a traffic safety object2110 is placed near the first detour lane line when it is disposedwithin a predefined distance (e.g., 0.5 meter) from the first detourlane line. In some embodiments, the first detour lane line is manuallymarked on the first image 2212 by hand. The computer system presents thefirst image 2212 to a user, and receives a user input defining the firstdetour lane line on the first image 2212. The first detour lane line isoptionally solid or dashed.

Further, in some embodiments, the computer device determines the firstdetour lane line by determining one or more of: a total number of detourpaths, a length of the first detour lane line, a number of objects onthe first detour lane line, object spacings between each two immediatelyadjacent traffic safety objects 2110, curvatures of the first detourlane line at the plurality of traffic safety objects 2110, and randomlygenerated deviations from the first detour lane line.

Additionally, in some embodiments, the first detour lane line definesthe detour path 2310 jointly with a second detour lane line. The seconddetour lane line optionally includes a solid or dashed lane marking thatexists on the road. Alternatively, in some embodiments, the seconddetour lane line includes another detour lane line (e.g., right detourlane lines 2314E, 2314F, and 2314G in FIG. 25D-27F) defined to placeanother set of traffic safety objects 2110. Specifically, the computerdevice determines the second detour lane line parallel to the firstdetour lane line. The second detour lane line is configured to definethe detour path 2310 on the road jointly with the first detour laneline. Positions are identified for a second plurality of traffic safetyobjects 2110 on or near the second detour lane line. The second detourlane line is optionally shorter than, longer than, or equal to the firstdetour lane line. In some embodiments, the second detour lane lineexists in the first image 2212. In some embodiments, the second detourlane line is manually marked by hand.

In some embodiments, the second image 2214 is associated with a groundtruth, and the ground truth includes the first detour lane line. Thecomputer device iteratively recognizes, by the machine learning system,a lane line of the detour path 2310 using the model 2206, compares therecognized lane line to the first detour lane line of the ground truth,and adjusts the model 2206 to match the recognized detour lane line tothe first detour lane line of the ground truth, e.g., using a loss.

In some embodiments, the detour path 2310 is adjacent to a road feature,and the road feature is one of a vehicle, a person, a bike, amotorcycle, a traffic sign, and a road sign. Further, in someembodiments, the computer device determines that the road feature ispartially occluded by a first traffic safety object 2110 based on depthvalues of the road feature and the first traffic safety object 2110. Thefirst traffic safety object 2110 is overlaid partially on the roadfeature. In an example, the first detour lane line crosses the roadfeature.

In some embodiments, the traffic safety object 2110 includes a conestructure that is otherwise known as a pylon, road cone, highway cone,safety cone, traffic cone, channelizing device, or construction cone.

In some embodiments, the computer system determines alternativepositions of the plurality of traffic safety objects 2110 in a firstcoordinate system (e.g., an IMU coordinate system) and converts thealternative positions of the plurality of traffic safety objects 2110 inthe first coordinate system to the positions of the plurality of trafficsafety objects 2110 in a camera coordinate system. Further, in someembodiments, the computer system obtains a camera intrinsic parameter Kand a conversion vector (e.g., M) between the first coordinate systemand the camera coordinate system. Based on the camera intrinsicparameter K and the conversion vector M, the alternative positions ofthe plurality of traffic safety objects 2110 in the first coordinatesystem are converted to the positions of the plurality of traffic safetyobjects 2110 in an image coordinate system.

In some embodiments, for each of the plurality of traffic safety objects2110, the computer system identifies one or more reference nodes in therespective copy of the image of the traffic safety object (e.g., a basecenter node of each cone), and determines a respective location of eachof the one or more reference nodes in a first coordinate systemcorresponding to a first space, and converts the respective location ofeach of the one or more reference nodes in the first coordinate systemto a respective location near a position of the respective trafficsafety object 2110 in a camera coordinate system. Further, in someembodiments, the computer system further obtains a camera intrinsicparameter K and the conversion vector (e.g., M) between the firstcoordinate system and a camera coordinate system. Based on the cameraintrinsic parameter K and the conversion vector, the respective locationof each of the one or more reference nodes in the first coordinatesystem is converted to the respective location near the position of therespective traffic safety object 2110 in an image coordinate system.

It should be understood that the particular order in which theoperations in FIG. 27 have been described are merely exemplary and arenot intended to indicate that the described order is the only order inwhich the operations could be performed. One of ordinary skill in theart would recognize various ways to augmenting vehicle training data(e.g., related to a detour path 2310 arranged by a plurality of trafficsafety objects 2110). Additionally, it should be noted that detailsdescribed with respect to FIGS. 1-26F are also applicable in ananalogous manner to the method 2700 described above with respect to FIG.27 . For brevity, these details are not repeated here.

In one or more examples, the functions described may be implemented inhardware, software, firmware, or any combination thereof. If implementedin software, the functions may be stored on or transmitted over, as oneor more instructions or code, a computer-readable medium and executed bya hardware-based processing unit. Computer-readable media may includecomputer-readable storage media, which corresponds to a tangible mediumsuch as data storage media, or communication media including any mediumthat facilitates transfer of a computer program from one place toanother (e.g., according to a communication protocol). In this manner,computer-readable media generally may correspond to (1) tangiblecomputer-readable storage media which is non-transitory or (2) acommunication medium, such as a signal or carrier wave. Data storagemedia may be any available media that can be accessed by one or morecomputers or one or more processors to retrieve instructions, codeand/or data structures for implementation of the embodiments describedin the present application. A computer program product may include acomputer-readable medium.

The terminology used in the description of the embodiments herein is forthe purpose of describing particular embodiments only and is notintended to limit the scope of claims. As used in the description of theembodiments and the appended claims, the singular forms “a,” “an,” and“the” are intended to include the plural forms as well, unless thecontext clearly indicates otherwise. It will also be understood that theterm “and/or” as used herein refers to and encompasses any and allpossible combinations of one or more of the associated listed items. Itwill be further understood that the terms “comprises” and/or“comprising,” when used in this specification, specify the presence ofstated features, elements, and/or components, but do not preclude thepresence or addition of one or more other features, elements,components, and/or groups thereof.

It will also be understood that, although the terms first and second maybe used herein to describe various elements, these elements should notbe limited by these terms. These terms are only used to distinguish oneelement from another. For example, a first electrode could be termed asecond electrode, and, similarly, a second electrode could be termed afirst electrode, without departing from the scope of the embodiments.The first electrode and the second electrode are both electrodes, butthey are not the same electrode.

The description of the present application has been presented forpurposes of illustration and description, and is not intended to beexhaustive or limited to the invention in the form disclosed. Manymodifications, variations, and alternative embodiments will be apparentto those of ordinary skill in the art having the benefit of theteachings presented in the foregoing descriptions and the associateddrawings. The embodiments are described in order to best explain theprinciples of the invention, the practical application, and to enableothers skilled in the art to understand the invention for variousembodiments and to utilize the underlying principles and variousembodiments with various modifications as are suited to the particularuse contemplated. Therefore, the scope of the claims is not to belimited to the specific examples of the embodiments disclosed.Modifications and other embodiments are intended to be included withinthe scope of the appended claims.

What is claimed is:
 1. A method for augmenting training images used forgenerating autonomous vehicle driving models, comprising: at a computersystem including one or more processors and memory: obtaining a firstimage of a road; identifying within the first image a drivable area ofthe road; obtaining an image of a traffic safety object; determining adetour path on the drivable area; determining positions for a pluralityof traffic safety objects to be placed adjacent to the detour path;generating a second image from the first image by adaptively overlayinga respective copy of the image of the traffic safety object at each ofthe determined positions; and adding the second image to a corpus oftraining images to be used by a machine learning system to generate amodel for facilitating at least partial autonomous driving of a vehicle.2. The method of claim 1, further comprising: training, using machinelearning, the model using the corpus of training images, including thesecond image; and distributing the model to one or more vehicles,including a first vehicle; wherein, in use, the model is configured toprocess road images captured by the first vehicle to facilitate at leastpartially autonomously driving the first vehicle.
 3. The method of claim1, wherein adaptively overlaying the respective copy of the image of thetraffic safety object at each of the determined positions comprisesscaling a respective size of the respective copy of the image of thetraffic safety object based on a respective position where therespective traffic safety object is to be placed.
 4. The method of claim1, wherein adaptively overlaying the respective copy of the image of thetraffic safety object at each of the determined positions comprisesadjusting an orientation of the respective copy of the image of thetraffic safety object based on a direction normal to the drivable areaat the respective position.
 5. The method of claim 1, wherein adaptivelyoverlaying the respective copy of the image of the traffic safety objectat each of the determined positions comprises adjusting one or moreimage properties of the respective copy of the image of the trafficsafety object.
 6. The method of claim 1, further comprising: obtaininginformation for a plurality of road features, including one or more of:a vehicle, a lane area, a shoulder area, an edge marking, a lanemarking, a shoulder barrier structure, a road divider, a traffic light,a traffic sign, a road sign, a pedestrian, and a bicycle, wherein thedetermined positions are determined based on the information for theplurality of road features.
 7. The method of claim 1, wherein the detourpath is defined by two substantially parallel detour lane lines, andpositions of one or more of the traffic safety objects are substantiallyon or near the detour lane lines.
 8. The method of claim 1, whereindetermining the detour path on the drivable area comprises: determininga first detour lane line that is adjacent to the detour path andconfigured to define the detour path on the drivable area; andidentifying positions for one or more of the plurality of traffic safetyobjects on or near the first detour lane line.
 9. The method of claim 8,wherein determining the first detour lane line comprises: determiningone or more of: a total number of detour paths, a length of the firstdetour lane line, a number of objects on the first detour lane line,object spacings between each two immediately adjacent traffic safetyobjects, curvatures of the first detour lane line at the plurality oftraffic safety objects, and randomly generated deviations from the firstdetour lane line.
 10. The method of claim 8, wherein determining thedetour path on the drivable area comprises: determining a second detourlane line parallel to the first detour lane line, the second detour laneline configured to define the detour path on the road jointly with thefirst detour lane line; and identifying positions for a second pluralityof traffic safety objects on or near the second detour lane line. 11.The method of claim 8, wherein the second image is associated with aground truth, and the ground truth includes the first detour lane line,the method comprising, iteratively: recognizing, by the machine learningsystem, a lane line of the detour path using the model; comparing therecognized lane line to the first detour lane line of the ground truth;and adjusting the model to match the recognized detour lane line to thefirst detour lane line of the ground truth.
 12. The method of claim 1,wherein the detour path is adjacent to a road feature, and the roadfeature is one of a vehicle, a person, a bike, a motorcycle, a trafficsign, and a road sign.
 13. The method of claim 12, wherein the methodfurther comprises: determining that the road feature is partiallyoccluded by a first traffic safety object based on depth values of theroad feature and the first traffic safety object, wherein the firsttraffic safety object is overlaid partially on the road feature.
 14. Themethod of claim 1, wherein the traffic safety object includes a conestructure.
 15. The method of claim 1, wherein determining the positionscomprises: determining alternative positions of the plurality of trafficsafety objects in a first coordinate system; and converting thealternative positions of the plurality of traffic safety objects in thefirst coordinate system to the positions of the plurality of trafficsafety objects in a camera coordinate system.
 16. The method of claim15, wherein determining the positions further comprises, for each of theplurality of traffic safety objects: obtaining a camera intrinsicparameter K and a conversion vector between the first coordinate systemand the camera coordinate system; wherein based on the camera intrinsicparameter K and the conversion vector, the respective location of eachof the one or more reference nodes in the first coordinate system isconverted to a respective location near a position of the respectivetraffic safety object in an image coordinate system.
 17. The method ofclaim 1, wherein determining the positions comprises, for each of theplurality of traffic safety objects: identifying one or more referencenodes in the respective copy of the image of the traffic safety object;determining a respective location of each of the one or more referencenodes in a first coordinate system corresponding to a first space; andconverting the respective location of each of the one or more referencenodes in the first coordinate system to a respective location near aposition of the respective traffic safety object in an image coordinatesystem.
 18. The method of claim 17, wherein determining the positionsfurther comprises, for each of the plurality of traffic safety objects:obtaining a camera intrinsic parameter K and a conversion vector betweenthe first coordinate system and a camera coordinate system; whereinbased on the camera intrinsic parameter K and the conversion vector, therespective location of each of the one or more reference nodes in thefirst coordinate system is converted to the respective location near theposition of the respective traffic safety object in the image coordinatesystem.
 19. A computer system, comprising: one or more processors; andmemory storing one or more programs configured for execution by the oneor more processors, the one or more programs comprising instructionsfor: obtaining a first image of a road; identifying within the firstimage a drivable area of the road; obtaining an image of a trafficsafety object; determining a detour path on the drivable area;determining positions for a plurality of traffic safety objects to beplaced adjacent to the detour path; generating a second image from thefirst image by adaptively overlaying a respective copy of the image ofthe traffic safety object at each of the determined positions; andadding the second image to a corpus of training images to be used by amachine learning system to generate a model for facilitating at leastpartial autonomous driving of a vehicle.
 20. A non-transitorycomputer-readable medium storing one or more programs configured forexecution by one or more processors of a computer system, the one ormore programs comprising instructions for: obtaining a first image of aroad; identifying within the first image a drivable area of the road;obtaining an image of a traffic safety object; determining a detour pathon the drivable area; determining positions for a plurality of trafficsafety objects to be placed adjacent to the detour path; generating asecond image from the first image by adaptively overlaying a respectivecopy of the image of the traffic safety object at each of the determinedpositions; and adding the second image to a corpus of training images tobe used by a machine learning system to generate a model forfacilitating at least partial autonomous driving of a vehicle.